Rag pipeline

Skill Amey-Thakur/AI-SKILLS/skills/llm-engineering/rag-pipeline

Plug-and-play skills and prompts for every AI coding agent

Install

npx -y skills add Amey-Thakur/AI-SKILLS --skill rag-pipeline

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Design a retrieval-augmented generation pipeline that answers from sources and can be trusted. Use when building or debugging RAG: chunking, embedding, retrieval, context assembly, and grounding.

SKILL.md

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RAG pipeline

Retrieval-augmented generation is only as good as what it retrieves. A wrong or missing chunk produces a confident wrong answer, so build the retrieval half with the same rigor as the generation half, and make every answer traceable to a source.

Method

Decide what a good answer needs before indexing anything. The questions the system must answer determine the chunk size, the metadata, and the retrieval strategy. A pipeline tuned for "find the one relevant sentence" differs from one tuned for "summarize this whole section".
Chunk on meaning, not on character count. Split at natural boundaries (headings, paragraphs, function definitions), keep chunks self-contained enough to stand alone, and carry metadata with each: source document, heading path, page or line. Overlap adjacent chunks slightly so a fact split across a boundary survives. Oversized chunks bury the signal; undersized chunks lose the context that makes them meaningful.
Retrieve hybrid, not one-signal. Keyword search (BM25 or full-text) catches exact terms and names; vector search catches meaning and paraphrase. Blend both, because each misses what the other finds. Retrieve more than you need, then rerank down to the best few.
Pack the context to the model's real window. Fit the top chunks to the available tokens after the system prompt, history, and reserved answer room, best first. When it does not all fit, drop the lowest-ranked chunks rather than truncating one mid-thought.
Ground the generation and demand citations. Instruct the model to answer only from the provided context, to say "the sources do not cover this" when they do not, and to cite the source for each claim. An answer that cannot point to a chunk is a hallucination with good manners.
Evaluate retrieval and generation separately. Most RAG failures are retrieval failures wearing a generation costume. Measure whether the right chunk was retrieved (recall on a labeled question set) apart from whether the answer was faithful to it. You cannot fix what you cannot localize.

Common failures

The answer is confidently wrong: the right chunk was never retrieved. Fix retrieval (chunking, hybrid search, reranking), not the prompt.
The answer ignores the context: weak grounding instruction, or the context arrived above the model's attention. Strengthen the instruction, shorten the context.
Citations are invented: the model was not given stable source ids to cite. Label each chunk and require the label.
Fresh data is missing: the index is stale. Re-index on a schedule that matches how fast the sources change.

Boundaries

RAG adds knowledge, not reasoning. If the task needs multi-step logic over the retrieved facts, that is a generation and prompting problem on top of retrieval, not more retrieval. And no pipeline rescues sources that do not contain the answer; say so rather than generating around the gap.