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A production RAG system is a pair of connected pipelines: one that prepares and secures source content for retrieval, and one that retrieves evidence, constructs context, and generates an answer for each query. The language model and search index matter, but reliable results also depend on representative test data, source permissions, traceable citations, and evaluation across both pipelines.
What belongs in a production RAG architecture?
Retrieval-augmented generation (RAG) gives a language model relevant external material at answer time. Instead of relying only on information stored in model parameters, the application retrieves passages from a source collection and includes selected evidence in the model’s context. That can make answers more relevant to private or changing content, but retrieving text does not by itself make an answer correct or authorized.
Design the system as two connected lifecycles:
- Content and indexing: collect and update authorized sources, extract their content, split and enrich it, create searchable representations, and keep the index synchronized with changes and deletions.
- Online query and answer: receive a question, retrieve and rank relevant evidence under the user’s permissions, assemble context, generate a response, and return useful source references.
Microsoft’s RAG solution design and evaluation guide describes this end-to-end view. It is a useful architectural pattern, not a requirement to use Azure services.
How do you build the content and indexing pipeline?
Start with the content and questions the application must support—not with a preferred embedding model or vector database. Decide which sources are in scope, what users are allowed to see, what decisions the system should help with, and what counts as a sufficient answer. For each representative question, identify the source passages that support an answer. Those examples become a working test set for parsing, chunking, retrieval, and answer behavior.
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Ingest, extract, and preserve provenance
A typical path is:
Source systems → ingestion or synchronization → parsing and extraction → chunking → metadata enrichment → embedding → search index or vector store → refresh and deletion handling.
Choose extraction for the source format. A text document may need straightforward parsing; PDFs, scanned pages, and images may require OCR, document extraction, or image understanding. Preserve source identifiers and useful fields—such as title, location, date, content type, and access attributes—so that retrieved passages can be traced, filtered, and cited. Plan how updates and deletions propagate; a stale index can return obsolete material even if the original source is correct.
Microsoft’s RAG preparation guidance and solution design guide describe splitting documents into semantically relevant chunks and enriching them with fields such as titles, summaries, and keywords.
Choose chunking by testing, not by a universal number
There is no single chunk size or overlap established here as best for every corpus. A useful chunk needs enough surrounding meaning to answer the kinds of questions users ask, while remaining focused enough that retrieval does not bring in large amounts of irrelevant material. Source structure, expected answer scope, retrieval behavior, and model context limits all affect that balance.
Compare chunking approaches against the representative questions and documents you assembled. Check whether the relevant passages are found, whether key context is split across boundaries, and whether the resulting answer uses the evidence appropriately. Change chunk rules only with an evaluation that can show the effect.
How should the online retrieval pipeline work?
For a standard query, the application accepts a question, searches an authorized index, selects and orders candidate passages, assembles context, calls the model, and returns an answer with source references. The search stage may use one method or several, depending on the query classes and corpus.
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Pick retrieval methods for the questions users ask
- Lexical or full-text search is useful when exact terms matter, including identifiers, product names, and named entities.
- Vector search finds semantically similar content even when the question and source use different wording.
- Hybrid search combines lexical and vector retrieval, which can help when users ask both exact-term and meaning-based questions.
- Metadata filters narrow results by properties such as date, category, or the user’s authorization.
- Query translation—for example rewriting, augmentation, or decomposition—can help with vague questions or questions spanning multiple sources.
- Reranking reorders a broader set of retrieved candidates to improve which passages appear most useful to the model.
Microsoft’s information retrieval guidance describes running text and vector searches together in Azure AI Search and combining rankings with Reciprocal Rank Fusion. It also treats reranking as a further precision step. These are implementation patterns, not guarantees of platform-independent quality: broader retrieval, rank fusion, and reranking should be compared on your own relevance results and end-to-end latency.
Keep retrieved context focused and attributable
More retrieved text is not automatically better. Select and order evidence to answer the question, retain enough context to interpret each passage, and pass source metadata alongside the text. The application can then show where an answer came from and help users inspect the supporting material. Decide what to do if the evidence is missing, incomplete, or contradictory: ask a clarifying question, state the limitation, or abstain rather than presenting unsupported certainty.
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Should you use classic or agentic RAG?
Use the simplest orchestration that reliably handles the query workload. Classic RAG follows a fixed retrieval flow. Agentic RAG adds a planner or agent that can choose steps, break a question into subquestions, query different sources or tools, and retrieve iteratively. That flexibility may help with complex, conversational, or multi-source questions, but introduces more decisions to test and more potential latency.
| Approach | How it works | Best fit to evaluate | Main trade-off |
|---|---|---|---|
| Classic RAG | A fixed sequence retrieves evidence, constructs context, and calls the model. | Questions handled by one predictable search flow; cases prioritizing simplicity, speed, or fine-grained control. | Less adaptable when a query needs dynamic source selection or several retrieval steps. |
| Agentic RAG | A planner or agent can decompose questions, choose tools or sources, and retrieve more than once. | Complex, conversational, or cross-source questions where dynamic planning can improve evidence gathering. | More orchestration and evaluation work; measure tool-selection accuracy, calls per request, retrieval efficiency, answer quality, and total latency. |
These trade-offs are workload-specific. Microsoft’s Azure AI Search RAG overview recommends agentic retrieval for new implementations in its own service context, particularly for complex or conversational queries and structured citations. It also describes classic RAG as appropriate for needs such as GA-only features, simplicity, speed, and finer pipeline control. Treat that as Microsoft’s product guidance, not a universal rule for every RAG system.
How do you enforce access control?
Indexing private content does not grant every user permission to retrieve it. Carry identity and authorization constraints into the retrieval operation, using filterable metadata or the selected platform’s access-control features as appropriate. Test the actual path end to end: a user should not be able to retrieve another user’s or tenant’s material through search, query rewriting, agent tools, or cached results.
Microsoft identifies granular access control as a RAG design challenge and discusses security trimming in its agentic retrieval guidance. The exact mechanics depend on the chosen search store and connectors, so verify how identities and permissions are represented and enforced for each source.
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How do you evaluate a RAG system before and after release?
Evaluate the components separately and the complete user-facing result together. A plausible answer can still be wrong because of an extraction error, a bad chunk boundary, a retrieval miss, a permission filter, poor context assembly, or generation that does not follow the evidence. A versioned set of realistic questions, source passages, and expected answers or supporting evidence lets the team isolate those causes and compare changes.
Measure retrieval and answer quality separately
- Content preparation: inspect whether important source text was extracted, indexed, and updated or deleted correctly.
- Retrieval: check whether passages that contain sufficient evidence appear among the results, and whether their ordering supports the task.
- Answer behavior: assess groundedness, completeness, relevance, correctness, and use of the supplied evidence. Treat groundedness and correctness as distinct: an answer may refer to retrieved context yet still interpret it incorrectly.
- Operations: measure latency across the full request, including any query transformation, reranking, or agent tool calls. Set targets for the application rather than borrowing an unsupported universal latency threshold.
For agentic flows, also review whether the planner selected the right tools and sources, how many calls it made, and whether the extra steps improved the final result enough to justify their cost in latency and complexity. Microsoft’s RAG evaluation guidance recommends assessing the phases and includes dimensions such as groundedness, completeness, utilization, relevance, and correctness. Model output can vary between runs, so compare aggregates or target ranges rather than drawing conclusions from one response.
Use a repeatable failure-to-fix loop
- Collect representative failures. Include wrong, incomplete, unsupported, and unauthorized answers as well as queries that work.
- Label the likely failing stage. Identify whether the cause is missing source content, extraction, chunking, indexing, retrieval, permissions, context assembly, or generation.
- Change one stage at a time. Keep the configuration and the specific change recorded so its effect can be interpreted.
- Rerun the evaluation set. Compare retrieval and answer measures alongside end-to-end latency, and inspect examples where results changed.
- Deploy with monitoring and a rollback path. Watch for regressions in quality, access behavior, and operational performance after release.
Microsoft’s design and preparation guides connect representative questions with content, while its evaluation guidance calls for assessing the different phases. Those sources do not establish production SLO values; choose targets based on your application’s requirements.
How should you compare managed cloud architectures?
Cloud services package different parts of ingestion, indexing, retrieval, and generation. Compare what each option actually provides for your corpus and operating model rather than assuming that a managed service removes the need for evaluation or authorization design.
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|---|---|---|
| Microsoft Azure | Azure AI Search documentation covers classic RAG, agentic retrieval, text and vector search, hybrid retrieval, filters, query transformation, and reranking. | Feature availability and maturity, source integration, access-control mechanics, retrieval flexibility, latency, and operational responsibilities. |
| AWS | AWS Prescriptive Guidance describes Amazon Bedrock Knowledge Bases, including retrieval-only and retrieve-and-generate API paths, traceability to sources, and connectors such as S3 and Confluence. | Current service behavior and connector support. The cited PDF’s document history identifies October 2024, so verify implementation details against current service documentation. |
| Google Cloud | The RAG reference architecture page lists options including managed vector search, AlloyDB-backed embeddings, GKE with Cloud SQL, and GraphRAG using Spanner Graph. | Fit of the data and graph choices, authorization, query flexibility, service maturity, latency, and who will operate each component. The page was last reviewed 2025-09-22 UTC. |
These are examples, not an exhaustive market survey or a ranking. Compare source integration, security model, required control over parsing and indexing, portability, operational effort, evaluation fit, and measured latency. The cited architecture sources do not provide comparable prices or a cross-vendor performance benchmark; estimate cost and performance using the intended region, configuration, and workload.
For a production design, keep the evidence trail from source to answer intact: know what entered the index, who may retrieve it, which passages supported a response, and how changes affect measured quality. That makes the architecture diagnosable and gives the team a basis for deciding whether additional retrieval complexity earns its place.
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