arXiv:2608.04330v1 Announce Type: new Abstract: Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens with little change. We turn this observation into prefix-removal probing and intr…
Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens with little change. We turn this observation into prefix-removal probing and introduce Right Reset (RR), which measures preservat…
<h1> RAG Content Pipeline in Production: 5 Decisions That Separate Working Systems from Demos </h1> <p>Every demo RAG system works. It retrieves something, hands it to an LLM, and produces a plausible answer. Every production RAG system fails — at least once — for reasons that ha…
<p>If retrieval is imperfect and the window is enormous, why not retrieve fifty chunks instead of five and let the model sort it out? Because recall and cost do not grow at the same rate, and because where a chunk sits in the context turns out to matter.</p> <h2> The temptation <…
dev.to — LLM tag
TIER_1English(EN)·Devanshu Biswas·
<p>Retrieval-augmented generation never feeds the whole document to the model — it feeds <em>chunks</em>. So the answer the model can give is bounded by what a single chunk contains. If chunking splits an idea in half, the top-retrieved chunk is half an answer, and no amount of c…
<p>In the first article, we explored why many RAG systems fail in production and established a key principle: retrieval quality determines answer quality. We also introduced the architecture behind production-grade RAG systems and explained why a simple "embeddings + vector datab…
<p><strong>Chunk overlap repeats the tail of each chunk at the start of the next one</strong>, so a fact that spans a boundary isn't split in half and lost to retrieval.</p> <p><strong>A little overlap helps; too much wastes tokens and storage.</strong> A common range is 10–20% o…
<p><strong>Fixed-size chunking cuts every N characters — simple, but it slices through sentences, paragraphs, and sections.</strong> Structure-aware chunking splits on the document's own boundaries (headings, paragraphs), keeping each chunk coherent.</p> <p><strong>Structure-awar…
<h1> RAG in Production in 2026: Beyond Naive Chunk-and-Embed </h1> <p>Every team building on LLMs eventually hits the same wall: the model knows a lot, but it doesn't know <em>your</em> data. Retrieval-Augmented Generation (RAG) is the standard answer — yet the default implementa…
