RAG (Retrieval-Augmented Generation) Cheat Sheet

Design retrieval-augmented pipelines covering chunking strategies, hybrid search, reranking, and evaluation of grounded LLM answers.

3 PagesIntermediateFeb 25, 2026

Chunk Documents for Retrieval

Split long text into overlapping chunks sized for the embedding model's context window.

python

from langchain_text_splitters import RecursiveCharacterTextSplittersplitter = RecursiveCharacterTextSplitter(    chunk_size=800,    chunk_overlap=120,    separators=["\n\n", "\n", ". ", " ", ""],)chunks = splitter.split_text(long_document)print(f"{len(chunks)} chunks, avg len {sum(len(c) for c in chunks)//len(chunks)}")

Hybrid Search (Dense + Sparse)

Combine keyword (BM25) and vector search scores for more robust retrieval.

python

from rank_bm25 import BM25Okapibm25 = BM25Okapi([c.split() for c in chunks])bm25_scores = bm25.get_scores(query.split())vector_scores = vector_index.similarity_scores(query, top_k=len(chunks))# reciprocal rank fusion of the two rankingsdef rrf_score(rank, k=60):    return 1.0 / (k + rank)combined = {}for rank, idx in enumerate(np.argsort(-bm25_scores)):    combined[idx] = combined.get(idx, 0) + rrf_score(rank)for rank, idx in enumerate(np.argsort(-vector_scores)):    combined[idx] = combined.get(idx, 0) + rrf_score(rank)

Rerank Retrieved Chunks

Use a cross-encoder to rescore the top candidates before sending them to the LLM.

python

from sentence_transformers import CrossEncoderreranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")pairs = [(query, chunk) for chunk in top_20_chunks]scores = reranker.predict(pairs)ranked = [c for _, c in sorted(zip(scores, top_20_chunks), reverse=True)]top_5 = ranked[:5]

Build a Grounded Prompt

Assemble retrieved context into a prompt that instructs the model to only answer from context.

python

SYSTEM = """Answer only using the CONTEXT below. If the answer isn'tcontained in the context, say you don't know. Cite the [source] for each claim."""context = "\n\n".join(f"[{c.metadata['source']}] {c.text}" for c in top_5)messages = [    {"role": "system", "content": SYSTEM},    {"role": "user", "content": f"CONTEXT:\n{context}\n\nQUESTION: {query}"},]

Common RAG Failure Modes

The most frequent ways a RAG pipeline breaks in production.

Chunk too large- dilutes relevance signal, buries the useful sentence in noise
Chunk too small- loses surrounding context needed to answer correctly
No reranking- top-k vector search alone often misses the best passage
Missing metadata filters- retrieval mixes documents across tenants/versions/dates
Stale index- source docs changed but the vector store was never re-embedded
No answer refusal- model hallucinates instead of saying context is insufficient

Pro Tip

Evaluate retrieval and generation separately — measure context recall (did we fetch the right chunk?) before measuring answer quality, since a perfect generator can't fix bad retrieval.

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