A reliable retrieval-augmented generation (RAG) system is not just a vector database connected to a language model. It is a sequence of stages—from document processing to retrieval, context assembly and answer generation—and each stage can fail independently. Start with the simplest pipeline that fits your queries, measure where it falls short, and add retrieval sophistication only when evaluation shows it helps.
What a RAG pipeline actually does
RAG supplies a language model with relevant information from an external knowledge source so it can answer with domain-specific context. It has two broad flows: ingestion, which prepares and indexes material, and query-time processing, which finds evidence and uses it to answer.
| Stage | What happens | Typical failure to investigate |
|---|---|---|
| Ingestion | Documents or other media are processed into chunks, enriched with metadata, converted into embeddings, and persisted in a search index. | Useful content is missing, extracted poorly, split in a way that loses meaning, or indexed without helpful metadata. |
| Retrieval | An orchestrator searches the index and selects candidate passages for a query. | The supporting passage is absent from results, or irrelevant material crowds it out. |
| Context assembly | Selected results are combined with the user’s question and prepared for the model. | Too little evidence, too much noise, or inadequate context reaches generation. |
| Generation | The model answers using the supplied query and context. | The answer misreads, ignores, or goes beyond the retrieved evidence. |
Microsoft’s RAG architecture guidance distinguishes the ingestion and query-time flows. That separation is useful operationally: “the model got it wrong” is not a diagnosis until you know whether the relevant source was available, retrieved, included in context, and used correctly.
How should you design a RAG pipeline?
Begin with a baseline whose behavior is easy to inspect: accept a query, search an index, assemble context from the top results, and call the model. Microsoft describes this fixed sequence as standard RAG and says it suits queries that map to one search against one index. Establish representative test queries and a target for answer quality before adding more moving parts.
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Preserve meaning before tuning retrieval
Chunking is an information-design choice, not just a number to optimize. The right approach depends on the source format and what the task needs to retrieve. Microsoft’s guidance covers sentence-based, fixed-size, custom, layout-analysis, and model-assisted chunking; it does not prescribe one universal method.
Use representative documents and questions to inspect what the index actually contains. Check whether a passage retains enough surrounding context to be understood, whether extraction preserved the useful material, and whether cleaning removed noise without discarding meaning. Consider indexing titles, summaries, or keywords as discrete metadata fields when they improve discovery or filtering.
A short chunk can lose the entity or time period that makes it meaningful. Anthropic’s Contextual Retrieval approach adds context to chunks to address that problem. It also adds an indexing step, so compare it with simpler chunking on your corpus rather than assuming the extra enrichment is worthwhile.
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Choose retrieval methods for the misses you observe
Vector search finds semantically similar content, which can help when a question and its source use different wording. Lexical BM25 search is useful for precise word and phrase matches, including identifiers and technical terms. These methods address different needs; combining them is an option, not a baseline requirement.
Anthropic describes a hybrid pattern that runs lexical and vector retrieval, merges and deduplicates their results using rank fusion, then supplies a top-K set to generation. Microsoft’s retrieval guidance describes a broader multi-stage pattern: retrieve a larger candidate pool, merge result lists (for example, with reciprocal rank fusion), rerank candidates, and trim them to a smaller context set.
| Approach | Strength to test | Trade-off to assess |
|---|---|---|
| Vector retrieval | Finding semantically related passages despite different wording. | Whether exact terms, identifiers, or phrases are reliably found. |
| Lexical retrieval (BM25) | Matching precise words and phrases. | Whether relevant passages are missed when the query uses different wording. |
| Hybrid retrieval | Combining semantic and lexical candidates when both kinds of matches matter. | Additional merging and tuning; whether the combined results improve measured relevance and coverage. |
| Reranking | Reordering a broader candidate set so stronger evidence moves nearer the top. | Extra latency and cost, plus the operational and data-handling implications of another model call. |
When should you use reranking?
Reranking is worth testing when retrieval finds the correct evidence but places it too low in the candidate list. It is less likely to fix a source that was never ingested or a query that retrieves no useful candidates. First check whether the needed passage is present among the results; then compare answer quality with and without reranking.
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Microsoft suggests starting with a moderate candidate set and tuning it against evaluation. Its example ranges and top-result counts are starting points, not constants to copy into every system. Measure relevance on your domain and test set, alongside latency and cost. If a hosted reranking service receives document content, check whether that transfer fits your security and compliance requirements.
How do you evaluate a RAG pipeline?
Measure retrieval and answer behavior separately, then assess the complete system. OpenAI’s accuracy guidance distinguishes retrieval failures—wrong or noisy context—from model behavior. A model cannot reliably compensate for evidence that is missing or overwhelmed by irrelevant passages.
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- Groundedness: Are the answer’s claims supported by the supplied context?
- Completeness: Does the answer cover the material parts of the question?
- Utilization: Did the model make appropriate use of relevant context rather than ignore it?
- Relevance: Does the response address the actual query?
- Operational fit: What latency, cost, and complexity did the chosen design add?
Microsoft recommends testing with representative media and queries, documenting parameters and results, evaluating both retrieval and end-to-end response measures, and aggregating outcomes across queries. Keep failed cases as regression examples so a change that helps one slice does not silently damage another.
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A practical failure-investigation loop
- Identify a failed or weak answer and the specific claim or information it needed.
- Verify that the source exists and was parsed into usable content.
- Inspect the relevant chunks and metadata to see whether they preserve the evidence and its context.
- Check retrieval results: did they contain a supporting passage, and was it ranked high enough to be included?
- Inspect the assembled context, then determine whether generation used the evidence correctly.
- Record the case and rerun it against later pipeline changes.
NIST’s overview of the TREC 2025 RAG track illustrates why the stages merit separate tests. It describes retrieval, generation with fixed retrieved context, end-to-end RAG, and relevance-judgment tasks. Its generation task asks for sentence-level citations to supporting segments. That is one evaluation design, not a requirement for every production application, but checking whether answer claims point to evidence can reveal failures that answer-level scoring misses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is agentic RAG worth the extra complexity?
Consider agentic RAG when the workload genuinely calls for multistep reasoning, query decomposition at runtime, dynamic source selection, or retrieval combined with actions. Microsoft presents those as reasons to consider moving beyond standard RAG’s fixed single-search flow. They are workload requirements to validate, not evidence that agentic retrieval is automatically more accurate.
OpenAI recommends reaching the accuracy target with simpler methods before adopting more complex RAG or fine-tuning. RAG already introduces retrieval tuning alongside model behavior, which makes iteration and regression management harder. A useful rule is to identify a measured failure and test the least complex change likely to address it:
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- For exact-term misses, test lexical or hybrid retrieval.
- For vague or complex questions, test query translation or decomposition.
- When correct evidence is retrieved but ranked too low, test reranking.
- When one fixed search cannot handle the task, test dynamic or agentic retrieval.
In each case, retain the change only if it improves representative evaluation without unacceptable cost, latency, privacy, or operational burden. The available guidance establishes no universally best vendor, chunk size, top-K value, embedding model, or reranker; those choices depend on the corpus, workload, constraints, and measured results.
When might you not need RAG?
Anthropic says that for a knowledge base smaller than 200,000 tokens—about 500 pages of material—it may be possible to include the whole knowledge base in the prompt instead of using RAG. Treat that as vendor guidance tied to its context and prompt-caching discussion, not a universal cutoff: feasibility depends on the model, the actual material, and the application’s constraints. Compare the simpler whole-context option where it fits rather than assuming every knowledge source needs a retrieval index.
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