RAG RELIABILITY IS A CONTEXT ENGINEERING PROBLEM, NOT A PROMPT PROBLEM
RAG reliability hinges on how you structure and retrieve context, not on prompt tweaks or chunk-size folklore. A teardown of failing production pipelines shows...
RAG reliability hinges on how you structure and retrieve context, not on prompt tweaks or chunk-size folklore.
A teardown of failing production pipelines shows hallucinations come from skipping fundamentals: weighting hybrid retrieval and understanding chunking semantics, not from the model itself. See the deep-dive on hybrid search tradeoffs and “layered abstraction debt” in this RAG post on DEV. Why Your RAG System Keeps Hallucinating
Most outputs are shaped by the 95% of context you assemble around the user prompt — role, task, injected knowledge, history, tools — not the 5% prompt. That framing clarifies why retrieval and context packing dominate outcomes. Why Your Prompt Is Only 5% of What the Model Sees
For enterprise PDFs, section-aware retrieval beats naive page chunks. When files ship no outline, reconstruct a table of contents to let RAG scope by section and chunk on headings. Reconstructing the Table of Contents a PDF Forgot to Ship
If you need evidence-first research, wire your workflow so the model compares sources, tracks claims, and verifies citations rather than summarizing pages. This guide shows a practical end-to-end structure. ChatGPT 5.5 for Research: Web Verification, Source Handling, Deep Research, and Synthesis Workflows
Most RAG failures are data and retrieval issues; fixing context assembly moves accuracy far more than model swaps.
Section-aware parsing and hybrid search weighting reduce hallucinations on exact-code and policy queries that trip pure semantic search.
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terminal
Run A/B on retrieval: pure vector vs BM25 vs weighted hybrid; measure exact-match queries (e.g., codes, IDs) and conceptual queries separately.
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terminal
Compare TOC-aware, heading-bounded chunking vs fixed-size chunks on a representative PDF set; track citation correctness and answer locality.
Legacy codebase integration strategies...
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Instrument your RAG stack to log query class, retrieved spans, and rerank scores; tune weights before swapping models or vector DBs.
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Add a preprocessing step to reconstruct PDF outlines for key corpora; backfill sections without reindexing everything at once.
Fresh architecture paradigms...
- 01.
Design for context engineering from day one: explicit role/task/knowledge layers and section-scoped retrieval APIs.
- 02.
Bake TOC extraction and heading-based chunking into your ingestion pipeline; choose retrieval that supports hybrid scoring and reranking.
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