New RAG research enhances LLM retrieval, unlearning, and faithfulness
ByPulseAugur Editorial·[196 sources]·
Multiple research papers are exploring advancements in retrieval-augmented generation (RAG) to improve the performance and efficiency of large language models. Apple's CLaRa framework unifies retrieval and generation in a continuous latent space for better compression and end-to-end optimization. Other research focuses on machine unlearning for RAG to remove sensitive information, test-time adaptation for RAG to handle domain shifts, and graph-based matching for improved multi-hop reasoning. Additionally, methods are being developed to quantify retriever-generator alignment and suppress internal model knowledge to enhance faithfulness in RAG systems.
AI
IMPACT
These RAG advancements aim to improve LLM accuracy, efficiency, and trustworthiness, potentially accelerating their adoption in complex applications.
RANK_REASON
Cluster consists of multiple research papers detailing new methods and frameworks for retrieval-augmented generation (RAG).
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval–generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs …
arXiv cs.CL
TIER_1English(EN)·Shang Wang, Tianqing Zhu, Dayong Ye, Wanlei Zhou·
arXiv:2410.15267v3 Announce Type: replace-cross Abstract: The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their powerful natural language generation capabilities. However, these models can inadvertently learn and retain sensitive information and h…
arXiv:2601.11443v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through the integration of external knowledge. However, when adapting RAG systems to sp…
Retrieval-Augmented Generation (RAG) significantly enhances the ability of Large Language Models (LLMs) to provide accurate and contextually relevant answers by dynamically integrating external databases. However, traditional RAG methods are primarily constrained by their relianc…
Retrieval-Augmented Generation (RAG) systems enhance large language models by retrieving relevant documents from external knowledge bases. Recent work by Sarthi et al. (2024) introduced RAPTOR, which organizes documents into hierarchical tree structures for efficient retrieval, b…
arXiv:2502.15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence. However, they remain susceptible to unfaithful generation, where outputs…
Mainstream industrial information retrieval systems (e.g., search and recommendation) are usually built upon Multi-Stage Cascade Architectures (MCAs), which balance effectiveness and efficiency through a coarse-to-fine ``retrieval-ranking'' pipeline. However, the optimization obj…
arXiv cs.CL
TIER_1English(EN)·Korbinian Randl, Guido Rocchietti, Aron Henriksson, Ziawasch Abedjan, Tony Lindgren, John Pavlopoulos·
arXiv:2601.21803v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground their outputs in external documents. However, the interaction between these components remains opaque, creating challenges for d…
arXiv:2607.04008v1 Announce Type: new Abstract: We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided candidate set and all retrieved evidence and final citations must remain within tha…
We present a candidate-constrained retrieval-augmented generation system for LongEval-RAG, where each query is associated with an organizer-provided candidate set and all retrieved evidence and final citations must remain within that set. The system combines deterministic provena…
arXiv cs.AI
TIER_1English(EN)·Valentin J. J. Kreileder, Johannes Reisinger, Andreas Fischer·
arXiv:2607.01852v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval…
Generative retrieval has become a popular paradigm for large-scale recommendation. However, it is typically trained with supervised next-item prediction objectives that do not directly optimize long-term user satisfaction. In this work, we formulate recommendation as a session-le…
Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval and answer quality compared to fixed-size and rec…
Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters. We evaluate if cluster-based semantic chunking improves retrieval and answer quality compared to fixed-size and rec…
arXiv cs.CL
TIER_1English(EN)·Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi, Gabriel Murray, Giuseppe Carenini, Issam H. Laradji·
arXiv:2607.00570v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are relevant and the retrieved bundle may contain stale, noisy, or conflicting evidence…
arXiv:2607.00394v1 Announce Type: cross Abstract: LLM agents increasingly rely on retrieval buffers to store and reuse past experience, yet the cache management policies governing these buffers remain largely ad-hoc. We formalize this as an online semantic cache replacement probl…
Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are relevant and the retrieved bundle may contain stale, noisy, or conflicting evidence. Existing contrastive decoding methods primaril…
LLM agents increasingly rely on retrieval buffers to store and reuse past experience, yet the cache management policies governing these buffers remain largely ad-hoc. We formalize this as an online semantic cache replacement problem with switching costs, where items are matched b…
arXiv:2606.28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance. However, high-dimensional geometry induces concentration phenomena that can re…
arXiv cs.CL
TIER_1English(EN)·Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)·
arXiv:2606.29959v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically retrieves a fixed number of passages for every query. This is wasteful when the reader already knows the answer, and it can be harmful when irrelevant or partially relevant passages d…
arXiv cs.AI
TIER_1English(EN)·Ian van Dort (University of Amsterdam), Maria Heuss (University of Amsterdam)·
arXiv:2606.28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using inline citations for verifiability. However, the faithfulness o…
arXiv cs.AI
TIER_1English(EN)·Qili Zhang, Qianren Mao, Yangyifei Luo, Yashuo Luo, Hanwen Hao, Zhilong Cao, Weifeng Jiang, Zhijun Chen, Junnan Liu, Feng Yan, Xiaolong Wang, Jinlong Zhang, Zhenting Huang, Zhixing Tan, Jie Sun, Bo Li, Jianxin Li, Philip S. Yu·
arXiv:2412.15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but als…
arXiv:2606.29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding. This assumption breaks down for complex queries, such as multi-hop or ambiguous questions, where top-k sel…
arXiv:2606.29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This was…
arXiv:2606.28337v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are often evaluated using final answer accuracy, even though their failures can originate from preprocessing, retrieval, context packing, or generation. This paper presents a controlled…
Retrieval-augmented generation (RAG) typically retrieves a fixed number of passages for every query. This is wasteful when the reader already knows the answer, and it can be harmful when irrelevant or partially relevant passages distract the reader. We formulate adaptive RAG as c…
Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding. This assumption breaks down for complex queries, such as multi-hop or ambiguous questions, where top-k selection tends to over-cover one semantic aspect whi…
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard on…
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard on…
<p>Recently, I had the opportunity to deliver a presentation on Retrieval-Augmented Generation (RAG) at my institute. Preparing for this session helped me understand not only what RAG is, but also why it has become one of the most important techniques in modern AI applications.</…
<p><em>This is Part 2 of a series on optimizing OpenSearch for production RAG. </em><a href="https://medium.com/towards-artificial-intelligence/opensearch-optimizations-for-production-rag-c335ac49f3e5"><em>Part 1 covered semantic retrieval</em></a><em>, meaning vector search with…
<p>While LLMs are great, there are some limitations in using LLMs: LLMs can hallucinate, presenting factually incorrect information when they don't know the answers, and their knowledge gets frozen at the time of training. That's when Retrieval Augmented Generation (RAG) addresse…
<figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*O4lEhDU-U9byg1vIhZpbFw.png" /><figcaption>RAG Image</figcaption></figure><h4>How to get accurate, source-backed answers out of AI — instead of confident guesses.</h4><p>If you’ve spent any time with ChatGPT, Clau…
dev.to — LLM tag
TIER_1Deutsch(DE)·Machine coding Master·
<h2> Ditch Naive Chunking: Late Chunking RAG in Spring AI </h2> <p>Naive text chunking breaks your RAG pipeline by slicing context at arbitrary token boundaries before your embedding model ever sees the text. Late chunking fixes this structural flaw by running a long-context tran…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> 2 Methods for Improving RAG Retrieval: A Real Client Case Study </h1> <p>Most RAG advice defaults to the trendy stuff — agentic RAG, multi-hop reasoning, exotic re-ranking pipelines. But a lot of the time, the actual fix for bad retrieval is much simpler than that. This is a…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<h1> Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% </h1> <p><em>How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10</em></p> <h2> The RAG Reality Check </h2> <p>Everyone ships RAG the…
<p><em>Part 1 of Retrieval-Augmented Self-Recall — the research track behind <a href="https://dev.to/gde03/claude-code-beyond-the-prompt-i-run-a-live-automated-trading-system-on-it-heres-the-whole-setup-478l">Claude Code, Beyond the Prompt</a>. All code is open source: <a href="h…
dev.to — LLM tag
TIER_1English(EN)·Solon Framework·
<p>Most RAG demos stop at one search. You embed a question, pull top-k chunks, paste them into a prompt, and hope the first hit was enough. That works for FAQ pages. It fails when the first query is vague, the right facts are split across documents, or the model needs a second, m…
dev.to — LLM tag
TIER_1English(EN)·AI Bug Slayer 🐞·
<p>I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about.</p> <p>So here is my…
dev.to — LLM tag
TIER_1English(EN)·Muhammad Zulqarnain·
<h2> The Hallucination Problem </h2> <p>You ask your AI: "What's our company's revenue for Q3 2026?"</p> <p>You get a confident, detailed answer. Total fabrication.</p> <p>This is hallucination. The model makes up answers when it doesn't have information.</p> <p>RAG solves this b…
Retrieval-Augmented Generation: An Engineer's Guide to Building RAG Systems with Your Own Data by Jeroen Herczeg is the featured book 📖 on Leanpub! The engineer's guide to RAG systems that survive a deploy. Link: https:// leanpub.com/retrieval-augmente d-generation # Ai # Softwar…
