Ragas
PulseAugur coverage of Ragas — every cluster mentioning Ragas across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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New research questions LLM factuality evaluation methods, favors RAGAS
A new paper explores the reliability of methods used to evaluate the factuality of large language models. Researchers developed a meta-evaluation framework that perturbs gold standard answers to test how well existing m…
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Open-source LLMs evaluated for ESG reporting tasks · 1 source tracked
A new paper evaluates the performance of seven open-source large language models (LLMs) for retrieval-augmented generation (RAG) tasks specifically within the environmental, social, and governance (ESG) domain. The stud…
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RAG evaluation suites miss prompt regressions, study finds
A recent analysis explored the effectiveness of Retrieval-Augmented Generation (RAG) evaluation suites in detecting prompt regressions. The study found that standard metrics like faithfulness and answer-relevancy failed…
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Top 5 LLM Evaluation Frameworks for Release Engineering Ranked
A recent analysis highlights Promptfoo as the leading LLM evaluation framework for release engineering, particularly for its CI/CD integration that can block builds on failed tests. DeepEval is recommended for Python-ba…
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Hybrid RRF retrieval fixes RAG failures on Kannada literature
A developer detailed the challenges of building a Retrieval-Augmented Generation (RAG) system for a scanned Kannada novel, highlighting that retrieval, not the language model, was the primary bottleneck. The initial app…
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RAGas framework optimizes Ethereum smart contracts for gas efficiency
Researchers have developed RAGas, a novel framework designed to optimize gas usage in Ethereum smart contracts. This system leverages retrieval-augmented generation (RAG) and a large language model to identify and autom…
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Workshop to cover production RAG with open models and benchmarking
A workshop focused on production retrieval-augmented generation (RAG) using open models will be held on August 29. Led by AI consultant Ben Auffarth, the event will cover hybrid retrieval methods, reranking techniques, …
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Student builds RAG evaluation tool to catch LLM failures missed by RAGAS
A first-year AI student developed RAG Sentinel, an open-source tool to evaluate Retrieval-Augmented Generation (RAG) systems, addressing limitations in existing tools like RAGAS. The tool focuses on critical metrics suc…
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LLM evaluation tools offer metrics, but critical challenges remain
A review of five popular LLM evaluation tools—Arize Phoenix, DeepEval, Future AGI, Langfuse, and Ragas—reveals that while they offer a wide array of pre-built metrics, these metrics represent only the easier 20% of the …
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LLM governance engine adds RAGAS faithfulness scoring to combat hallucinations
A developer has enhanced an LLM governance engine by integrating RAGAS faithfulness scoring, which measures how well a model's response aligns with provided context. This new feature complements the existing PII firewal…
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LLM observability platforms diverge on advanced features as market booms
The LLM observability and evaluation platform market is rapidly expanding, with projections reaching $9.26 billion by 2030. Platforms are diversifying into AI-native tools, open-source evaluation libraries, AI gateways,…
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New study evaluates RAG pipeline for scientific question answering · 2 sources tracked
Researchers have introduced SciRet, a study examining retrieval-augmented generation (RAG) for scientific question answering using the CORD-19 dataset. The study evaluates a fixed RAG pipeline across three different cor…
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OpenAI's Promptfoo Acquisition Sparks Debate on LLM Evaluation Independence
The acquisition of Promptfoo by OpenAI has prompted a re-evaluation of LLM evaluation tools, highlighting concerns about vendor dependency and cost. The author proposes an alternative approach using a custom-trained cla…
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LLM evaluation metrics show stark differences in detecting AI fabrications
A recent experiment comparing two popular LLM-as-judge faithfulness metrics, Ragas and DeepEval, revealed significant discrepancies in their ability to detect fabricated information. While both metrics were applied to t…
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AI evaluation gap dubbed 'Watermelon Effect' after real-world use fails tests
An AI developer discovered a significant gap between their AI tutor, ARIA, and its real-world performance, a phenomenon they've termed the "Watermelon Effect." While standard evaluation metrics like DeepEval and Ragas s…
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New tool 'muteval' tests LLM evaluation robustness
Ashwin Ugale has developed a new tool called muteval, inspired by mutation testing in software engineering, to evaluate the robustness of Large Language Model (LLM) evaluation suites. Muteval deliberately degrades a sys…
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LLM judges introduce systematic biases, skewing evaluations
Using Large Language Models (LLMs) as judges for evaluating other LLM outputs introduces systematic biases, such as position, verbosity, and self-preference, which cannot be averaged out like random noise. These biases …
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Developer applies unit testing principles to AI releases for quality control
A developer has implemented an "eval-gated" release process for their Retrieval-Augmented Generation (RAG) copilot project, Atlas, treating quality and cost metrics like unit tests for code. This system blocks merge req…
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LLM-as-judge CI gates incur unexpected costs; deterministic alternatives offer savings
An engineer discovered that using LLM-as-judge metrics for CI/CD evaluation gates incurs significant, ongoing costs. These gates, which assess pull requests, can generate substantial bills due to repeated API calls to m…
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Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics · 8 sources tracked
This series of articles details the creation of production-grade evaluation pipelines for Large Language Models (LLMs), moving beyond subjective "vibe checks" to implement automated metrics. The authors emphasize the ne…