A strong RAG knowledge base retrieves evidence that is both relevant and sufficient to answer the question—not the fewest passages or the largest pile of context. Tune precision and evidence coverage against representative queries, then check whether answers are complete, correct, attributable, and appropriately withheld when the corpus lacks the answer.
What precision and breadth mean in RAG
Retrieval-augmented generation (RAG) fetches material from an external or private corpus and places it in a language model’s prompt as grounding context. It is useful when answers depend on information that is private or changes more often than a model’s built-in knowledge. Microsoft Learn describes the basic pattern: “RAG addresses this by retrieving relevant content from your data and including it in the model input.”
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Precision is how much of the retrieved material is genuinely useful to the query, especially near the top of the ranked results. Breadth, as used here, means whether the context covers the distinct evidence needed to answer fully. It does not mean retrieving indiscriminately more text.
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Why relevance alone is not enough
A passage can be topically relevant yet fail to contain the fact needed to answer. Conversely, a correct answer may depend on several pieces of evidence that are individually less obvious from the wording of the question. For this reason, evaluate both what retrieval returns and what the answer actually needs.
In “Deeper insights into retrieval augmented generation: the role of sufficient context,” published May 14, 2025, Google Research’s Cyrus Rashtchian and Da-Cheng Juan put the distinction this way: “But we believe that the context’s relevance alone is the wrong thing to measure — we really want to know whether it provides enough information for the LLM to answer the question or not.” The implication is to assess whether the evidence supports a complete answer, not merely whether retrieved text looks related.
Completeness also requires a safe response when the corpus does not contain the answer. The system should be able to abstain or clearly state that available evidence is insufficient, rather than treating a plausible-sounding completion as grounded.
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Content preparation, chunking, indexing, and query configuration all influence which evidence can be found. Microsoft’s Azure AI Search documentation describes large documents being divided into smaller portions so those portions can match independently. That flexibility helps retrieval target passages, but splitting can also separate a fact from its qualification or surrounding context.
| Choice | Potential contribution | What to evaluate |
|---|---|---|
| Keyword or lexical retrieval | Matches exact terms and named entities. | Ranking relevance, wording variants, and missed paraphrases. |
| Vector or semantic retrieval | Can match related concepts and paraphrases. | Coverage of relevant evidence, false matches, and behavior with domain terminology. |
| Hybrid retrieval | Combines keyword and vector signals; Microsoft describes it as a way to increase recall. | Precision and recall at the selected cutoff, answer completeness, latency, and cost. |
| Classic single-query RAG | Uses a simpler orchestration path without an LLM query-planning step. | Query quality, retrieval relevance, complexity, and speed. |
| Agentic or multi-query retrieval | Can use conversation context, focused subqueries, semantic ranking, and structured grounding. | Evidence coverage, attribution, added components, latency, and cost. |
| Chunking strategy | Determines which text spans can match independently. | Context preservation, passage relevance, answer completeness, and indexing cost. |
These are options to benchmark, not a universal ranking. Microsoft’s descriptions of classic and agentic retrieval refer to capabilities in its documented architecture: classic RAG avoids the query-planning step and is simpler and faster in that description, while agentic retrieval supports context-aware planning and multiple focused subqueries. That product-specific framing is not proof that one approach always produces better answers.
Chunk to preserve evidence, not to hit a magic length
A chunk should be large enough to preserve the relationships needed to interpret its contents, but focused enough to retrieve without dragging in unrelated material. Document structure and corpus needs matter: a section with a definition and its exception may need to remain together, while a long, loosely connected page may benefit from smaller retrievable spans. There is no single best chunk length established for every corpus.
A 2026 LREC paper by Paulo Roberto de Moura Júnior and coauthors, “Adaptive Chunking: Optimizing Chunking-Method Selection for RAG,” proposes selecting chunking methods using five intrinsic measures: References Completeness (RC), Intrachunk Cohesion (ICC), Document Contextual Coherence (DCC), Block Integrity (BI), and Size Compliance (SC). In the paper’s evaluated legal, technical, and social-science corpus, the authors report 72% answer correctness, compared with 62–64% for their baselines, and 65 successfully answered questions compared with 49. Those are results from that study’s corpus and evaluation, not expected gains for a different knowledge base.
Consider query enrichment as an experiment
Retrieval quality can also be affected by how queries are represented. The 2024 paper “Meta Knowledge for Retrieval Augmented Large Language Models” proposes adding document metadata, synthetic question-answer pairs, and summaries of document clusters to augment queries. In its experimental setup, the authors report that augmented-query matching significantly outperformed traditional chunking-based pipelines (p < 0.01), with further reported precision and recall improvements from its meta-knowledge approach. Treat this as a research direction to test against your own corpus and query set, not a default workflow.
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Build a representative, versioned set of questions before comparing configurations. Include questions with different evidence demands so a retrieval change cannot appear successful just because it improves one easy query type.
- Exact-fact questions and paraphrases of the same intent.
- Multi-part questions that require several facts or qualifications.
- Questions whose answer draws on more than one document.
- Questions for which the corpus does not contain sufficient evidence.
Run competing configurations on the same set of questions and, where possible, under similar context or token budgets. Track more than ranking quality:
- Retrieval: relevance and precision at a chosen k, plus recall or coverage of required evidence.
- Context sufficiency: whether retrieved passages contain the evidence needed to answer each question.
- Answer quality: correctness and completeness, including whether important qualifications are retained.
- Attribution: whether answer sentences are supported by the cited or otherwise identified source passages.
- Abstention: whether the system withholds an answer when evidence is missing.
- Operations: latency, token use and cost, throughput, and index size where relevant.
- Security: permission correctness and whether users can retrieve material they are not allowed to see.
Use errors to identify which part of the pipeline needs attention. A missed paraphrase suggests a query or retrieval issue; a retrieved passage that lacks a necessary exception may point to chunk boundaries or source organization; sufficient evidence followed by an incomplete answer points toward answer construction or prompting. This diagnosis helps avoid responding to every failure by simply increasing the number of retrieved passages.
The Tool Desk
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Operational limits and safeguards
RAG can ground answers in retrieved material, but it does not guarantee correctness or eliminate unsupported claims. Microsoft documents that poor content preparation, retrieval configuration, or prompts can harm output quality, and that irrelevant or incomplete passages can still lead to inaccurate answers.
Quick Recap
- Account for added compute and context: retrieval and embedding work add operational costs and latency, while retrieved passages use prompt tokens. Large indexes can slow retrieval; too many passages can exceed the model’s context budget.
- Enforce permissions during retrieval: apply access controls before material reaches the model, and test for leakage across users and roles.
- Treat retrieved text as untrusted input: documents may contain instructions or prompt-injection attempts. Retrieval is not a reason to grant source text authority over system rules.
- Monitor changing sources: for private or frequently updated knowledge, keep indexing and content preparation aligned with the source material so stale passages do not undermine otherwise relevant retrieval.
A practical tuning sequence
- Define answer evidence. For each representative query, record the facts, qualifications, and source spans needed for a complete supported answer.
- Establish a baseline. Record the current retrieval method, chunking, query settings, context budget, and answer behavior so later comparisons are meaningful.
- Change one meaningful factor at a time. Compare lexical, vector, or hybrid retrieval; chunking variations; or query decomposition where relevant. Keep the query set and budget comparable.
- Review both retrieval and answers. Find whether failures come from missing evidence, noisy results, incomplete context, weak attribution, or failure to abstain.
- Choose using trade-offs. Select the configuration that meets your task’s evidence-coverage and answer-quality needs without unacceptable token use, latency, cost, or permission risk.
- Re-evaluate after changes. Version the query set and repeat evaluation when the corpus, chunking, index, retrieval settings, or answer model changes.
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