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A production RAG system is two connected pipelines: one that turns authorized source material into searchable, maintainable evidence, and another that retrieves the right evidence for each question and uses it to generate a grounded response. A vector database and an LLM are only pieces of that design. Build the ingestion, permissions, retrieval, answer behavior, and evaluation loops together—and choose the simplest retrieval flow that meets the workload.
What belongs in a production RAG architecture?
Retrieval-augmented generation (RAG) supplies a language model with relevant material retrieved from a source collection. The system must do more than find semantically similar text: it must keep its index current, respect who may see each source, assemble useful context, and handle cases where evidence is missing or contradictory. Microsoft’s RAG solution design and evaluation guide treats RAG as an end-to-end design problem, including data preparation and evaluation.
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A practical architecture has two lifecycles:
- Content and indexing: collect authorized content, parse it, divide it into useful passages, attach metadata, create searchable representations, and keep the index synchronized with its sources.
- Query and response: authenticate the user, interpret the question, retrieve and rank permitted evidence, construct context, generate an answer, and return source references or an appropriate abstention.
These are not independent tracks. Index metadata affects filtering and citations; query behavior determines what retrieval must support; and evaluation results show which stage needs improvement.
How do you build the content and indexing pipeline?
Start with the sources and decisions the application must support, not with a preferred embedding model or chunk size. Assemble representative documents and realistic questions, and identify the passages that provide sufficient evidence for each question. That test set gives you a way to compare parsing, chunking, and retrieval choices against the actual content. Microsoft’s preparation guide likewise connects representative content with the questions used to test a RAG system.
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A typical indexing path is:
Source systems → ingestion and synchronization → parsing and extraction → chunking → metadata enrichment → embedding and indexing → refresh and deletion handling
Parse for the source format
Extract text and structure in a way that preserves meaning. A plain-text page, a PDF with tables, and a scanned image may need different handling; PDFs and images can require OCR, document extraction, or image understanding. Retain stable source identifiers and useful attributes such as title, date, category, and access scope so that retrieved passages can be traced, filtered, and maintained.
Chunk around meaning and answer scope
Split documents into passages that preserve enough context to answer likely questions without making every retrieval result unwieldy. There is no universal token length or overlap established by the cited guidance. The useful choice depends on source structure, the scope of expected answers, retrieval behavior, and the model’s context limits. Test alternatives on representative documents and queries, then compare retrieval and answer quality rather than adopting a fixed recipe.
Enrich and maintain the index
Metadata such as a title, summary, keywords, source identifier, and permission attributes can help retrieval and downstream citation. Define how additions, edits, and deletions reach the index; stale or deleted content can otherwise remain available to search. Track the version of parsing and indexing configuration used so changes can be evaluated and, if needed, reversed.
How should the retrieval layer find useful evidence?
Different questions benefit from different retrieval signals. Exact product codes and names may be best served by lexical search; a question phrased differently from the source may benefit from vector search. Hybrid retrieval combines text and vector searches, and a later ranking stage can reorder candidates before they are sent to the model. Microsoft’s information retrieval guidance describes hybrid search and reranking as design options, not platform-independent performance guarantees.
| Retrieval method | Useful when | Trade-off to test |
|---|---|---|
| Lexical or full-text | The question depends on exact words, identifiers, or named entities. | May miss relevant material expressed with different vocabulary. |
| Vector | The question and source passage express similar ideas in different words. | Semantic similarity alone may return passages that are related but not sufficiently relevant. |
| Hybrid | Queries need both exact-term matching and semantic matching. | Measure the combined results on the query set; ranking behavior depends on implementation. |
| Reranking | A broader candidate set needs a more precise ordering before context is assembled. | Can improve the relevance of selected context, but adds processing and latency. |
Metadata filters can narrow results by attributes such as date, content type, or authorization. Query transformations—including rewriting, augmentation, or decomposition—can help with vague or multi-part questions. Use these only where tests show a real retrieval problem: extra stages can add complexity and time.
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For each query class, compare retrieval quality and end-to-end latency. A reranker is worthwhile only if its contribution to the answers justifies its cost in processing time and system complexity. Likewise, hybrid search is not automatically better for every workload.
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Use a fixed retrieval flow when one predictable search is enough. Consider agentic retrieval when questions require planning, decomposition, dynamic source selection, or multiple retrieval steps. Agentic behavior expands what the system can attempt, but also introduces more decisions to test and more opportunities for tool selection or orchestration to fail.
| Approach | How it works | Best fit and evaluation focus |
|---|---|---|
| Classic RAG | A fixed sequence accepts a question, retrieves evidence, assembles context, calls the model, and returns an answer. | Predictable queries where simplicity, speed, or fine-grained control over the pipeline matters. Evaluate retrieval and answer quality alongside latency. |
| Agentic RAG | A planner or agent can decompose a question, choose sources or tools, retrieve iteratively, and decide whether more evidence is needed. | Complex, conversational, or multi-source questions. Also measure tool-selection accuracy, calls per request, retrieval efficiency, and end-to-end latency. |
Microsoft’s Azure AI Search overview recommends agentic retrieval for new implementations in its Azure service context, particularly for complex or conversational questions and structured citations. The same guidance identifies cases for classic RAG, including a need for general availability, simplicity, speed, or detailed pipeline control. That is product-specific advice, not a universal requirement to adopt agentic RAG.
In Microsoft’s agentic retrieval guidance, 3–5 retrieved results per tool call is a starting recommendation, not a general optimum. Treat it as a value to evaluate for that implementation and workload, not a default that applies to every RAG system.
How should answers use evidence and handle access?
Make grounding and abstention explicit
Tell the model and application how retrieved material should support an answer, what output structure is required, how source references should be attached, and what to do when evidence is insufficient or conflicting. Preserve source metadata through context construction so the response can expose provenance. Define whether the system should ask a clarifying question or abstain when it cannot support an answer. Supplying context does not by itself establish that a response is correct; Microsoft’s evaluation guidance distinguishes groundedness from correctness.
Apply permissions during retrieval
Indexing private content must not make it available to every user. Carry identity and authorization constraints into the retrieval path, using filterable metadata or the selected platform’s access controls as appropriate. Test with multiple users or tenants to verify that a requester cannot retrieve another party’s material. Exact security mechanics depend on the search store and connectors, so validate them for the services and identity model you deploy.
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How do you evaluate and improve a RAG system?
Create a versioned evaluation set containing representative questions and the source passages that provide adequate answers. Evaluate the pipeline in stages—extraction, chunking, indexing, retrieval, context assembly, and generation—so a poor result can be traced to a specific cause. Microsoft advises: “You should evaluate each of these phases and ensure that the results meet your expectations.”
Measure the dimensions that matter to the application. Microsoft’s guidance includes groundedness, completeness, utilization, relevance, and correctness. For an agentic flow, inspect tool selection and calls per request as well. Track latency and efficiency alongside answer quality; the sources do not establish universal production latency targets or cross-vendor accuracy benchmarks.
- Collect representative failures. Include questions the system answered poorly, not just successful demonstrations.
- Label the failure stage. Determine whether the problem was missing source content, parsing or chunking, retrieval, authorization, context construction, or generation.
- Change a specific stage. Keep the configuration change identifiable so its effect can be distinguished from other changes.
- Rerun the evaluation set. Compare quality and latency across the same workload, reviewing aggregates or target ranges rather than relying on a single model output.
- Deploy with monitoring and rollback. Watch for regressions in answer behavior, retrieval, permissions, and operating performance, and retain a way to restore a known configuration.
Model outputs can vary between runs, so a single answer is not a reliable system-level evaluation. Record configuration and results for meaningful changes and repeat the same evaluation process after release.
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Managed services can reduce the amount of infrastructure and integration a team operates, while custom pipelines can provide more control over parsing, indexing, retrieval, and orchestration. Compare options against your sources, security needs, operations capacity, and evaluation plan rather than looking for a universal winner.
- Source integration: Does the service connect to the systems and formats your content actually uses?
- Control: Can you customize parsing, chunking, metadata, query transformation, and ranking to the degree the workload requires?
- Authorization: Can permissions be enforced and tested at retrieval time for your identity and tenancy model?
- Operational ownership: Which ingestion, index refresh, monitoring, and failure-recovery tasks remain yours?
- Flexibility and portability: Can the retrieval pattern and data model evolve without unacceptable coupling?
- Measured fit: Does the implementation meet your quality, latency, and cost requirements on your own workload?
Official implementation references illustrate different approaches, but they are not an exhaustive market comparison. Microsoft documents classic RAG and agentic retrieval in Azure AI Search. AWS Prescriptive Guidance describes Amazon Bedrock Knowledge Bases, including retrieval-only and retrieve-and-generate API paths, source traceability, and connectors such as S3 and Confluence; its cited PDF document history identifies October 2024, so verify implementation details against current service documentation. Google Cloud’s RAG reference architectures list options including managed vector search, AlloyDB-backed embeddings, GKE with Cloud SQL, and GraphRAG using Spanner Graph; that page was last reviewed on 2025-09-22 UTC. See the AWS options guide and Google Cloud RAG reference architectures.
Service features and maturity can change. Confirm current availability and status in the provider’s documentation before choosing a specific feature. Do not infer a cost or performance winner from architecture pages: meaningful comparison requires the same workload, region, configuration, and current measurements.
What does a production-ready RAG decision look like?
A dependable design starts with representative evidence and queries, keeps source content and access rules current, uses retrieval methods suited to query types, and evaluates each stage as well as the final answer. Begin with fixed retrieval when it meets the need; add agentic planning only when complex questions justify its additional orchestration and evaluation burden. Treat every chunking, ranking, model, and cloud-service choice as a workload-specific decision to validate.
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