Send the LLM a selected evidence set—not simply every retrieved result or the top few by score. The passages should be relevant to the actual question, retain enough context to interpret them, collectively cover the facts needed for an answer, and fit your latency and token budget. If they do not, retrieve again or abstain rather than treating a full prompt as proof.
What makes retrieved context worth sending?
A retrieval list is a candidate pool, not an answer key. A high rank or similarity score is system- and query-dependent; it is not a calibrated probability that a passage supports the answer. A passage may be on topic yet omit the decisive detail, lose the entity or date needed to interpret it, or conflict with another source.
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Google Research defines context as sufficient when it contains all information necessary to provide a definitive answer; incomplete, inconclusive, or contradictory context is insufficient. That distinction matters: relevance asks whether a passage relates to the query, while sufficiency asks whether the selected evidence supports answering it. Google Research explains the distinction and its implications for generation.
How should you choose which passages reach the model?
Use a staged policy that preserves candidates until you can judge them against the full information need. For a compound request, first spell out its subquestions; the evidence set must cover each one, not just the most prominent part.
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Make the query answerable
For a conversational follow-up, rewrite the latest message as a standalone query that includes the needed conversational context. For a multi-part question, identify the distinct facts or subquestions an adequate answer requires. NVIDIA documents optional query rewriting in its RAG query-to-answer pipeline; set-wise retrieval research likewise treats coverage of multi-hop information needs as a distinct problem.
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Retrieve a broad candidate pool
Use semantic retrieval to find conceptual matches and lexical retrieval when exact terms, names, identifiers, or codes matter. Combining and deduplicating keyword and vector results can cover different matching needs: Anthropic describes this approach, and Microsoft recommends hybrid keyword-and-vector queries to improve recall. Whether both are useful depends on the corpus and workload. See Anthropic’s Contextual Retrieval article and Microsoft’s Azure AI Search RAG overview.
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Restore context stripped by chunking
A chunk can lose the document-level context that makes its statements interpretable: for example, which entity a pronoun refers to, what period a figure covers, or what a section is about. Preserve source metadata and enough surrounding material to recover that context. One indexing option described by Anthropic is to prepend concise, document-specific context to chunks before embedding and retrieval.
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Rerank before narrowing
A reranker scores a larger candidate pool against the user’s query, allowing the system to retain a smaller, more targeted set for generation. This is a filtering stage, not a guarantee of completeness: a reranker can still prefer several passages about one subquestion over a complementary passage that supplies another required fact. Measure whether reranking improves your answers enough to justify its additional runtime cost.
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Check coverage and conflicts as a set
Inspect whether the retained passages support every requested fact, identify relevant entities and dates, and leave disagreements visible. Several individually relevant snippets can collectively miss a necessary link in a multi-hop answer. The ACL 2025 paper “Shifting from Ranking to Set Selection for Retrieval Augmented Generation” studies selection for collective completeness on multi-hop benchmarks; its results should not be assumed to apply to every query class without evaluation.
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Generate only within the evidence boundary
Instruct the model to ground claims in the selected material, identify missing or conflicting evidence, and avoid filling gaps with unsupported assertions. When context is inadequate, the policy can trigger another retrieval attempt or abstention. A populated prompt is not itself a signal that an answer is grounded.
How many chunks should you pass to the LLM?
There is no universal chunk count. The useful number depends on the question, chunk size, corpus, retrieval and reranking policy, context window, and the cost of latency and tokens. More passages raise the chance of including useful evidence, but redundant or distracting context can make generation worse; a small set can be cleaner but omit a needed fact.
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Anthropic reports that 20 chunks performed better than 5 or 10 in the configurations it tested, while also warning that extra context can distract and recommending experiments on the actual use case. Treat that as evidence against assuming a tiny top-k is always best—not as a recommended default for another system. Tune the count and selection policy against answer quality and resource cost together.
Should you rerank retrieved documents?
Consider reranking when a broader first-stage retrieval improves recall but leaves too many weak or redundant candidates for the generation prompt. Compare otherwise similar policies with and without reranking on representative questions, recording answer correctness, evidence coverage, latency, and cost. Keep it only if the quality gain justifies its added runtime burden.
Anthropic’s 2024 evaluation reported a top-20 retrieval failure rate of 5.7% for its baseline configurations. In that evaluation, contextual embeddings reduced the rate to 3.7% (35% lower); contextual embeddings combined with contextual BM25 reduced it to 2.9% (49% lower); adding reranking to that combination reduced it to 1.9% (67% lower). These are vendor-reported experimental results under Anthropic’s cross-domain methodology, not expected gains for an arbitrary corpus or production workload. The article’s recommendation is concise: “Always run evals.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you tell if the evidence is enough?
Build an evaluation set from representative questions, including follow-ups, exact-identifier lookups, compound requests, multi-hop questions, and cases with incomplete or conflicting sources. For each, establish what evidence is needed before comparing policies. Evaluate the selected context and the generated answer separately: retrieval can miss required evidence, while generation can fail to use evidence that is present.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Coverage and correctness: Does the selected set support each required fact, and is the final answer correct and grounded in those passages?
- Precision and recall: Does the policy find exact matches without losing semantic paraphrases or complementary evidence?
- Redundancy and diversity: Are several chunks repeating one point while another necessary point is absent?
- Context integrity: Can a reader identify the relevant source, entity, date, and surrounding explanation?
- Efficiency: What do a larger candidate pool, query rewriting, reranking, and longer prompts add in latency and token cost?
- Failure behavior: Does the system detect missing or contradictory support and retrieve again or abstain?
Google Research reports at least 93% classification accuracy for its optimized prompted sufficient-context autorater on the evaluation described in its May 2025 article. That is a study result, not a production guarantee; any automated sufficiency check should be validated against the target workload and its failure cases.
What should happen when retrieved context is insufficient?
Make insufficiency an explicit system state, not something the generator must silently work around. Depending on the failure, retrieve again with a rewritten query, broaden or change the retrieval method, or ask for clarification if the question itself is ambiguous. If the necessary evidence remains unavailable—or sources disagree in a way the system cannot resolve—abstain or state the limitation rather than present an unsupported definitive answer.
This matters because adding context does not automatically improve judgment. Google Research reports that context can reduce a model’s tendency to abstain appropriately when evidence is insufficient. Its proposed selective-generation approach combines a context-sufficiency assessment with model confidence; neither a populated prompt nor model confidence alone should be treated as proof of support.
When is a simple retrieval pipeline enough?
Choose complexity for the query workload, not because a more elaborate architecture sounds safer. A classic retrieve-and-rerank pipeline may fit requests that are clear and can be answered from a focused set of passages. Conversational or complex requests may benefit from rewriting or query planning that identifies multiple information needs. Microsoft positions classic RAG around simplicity, speed, and fine-grained control, and agentic retrieval around complex or conversational queries and structured cited responses. Compare these options on your own workload, including failure behavior and operational cost.
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