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Retrieval-augmented generation (RAG) lets an AI application answer questions using selected documents or other knowledge sources without retraining its language model every time that material changes. The application retrieves relevant passages for each question and gives them to the model as context. Building a useful RAG system means getting that retrieval right, evaluating the answers, and controlling who can access the underlying data.
What RAG is—and what it does not do
RAG combines information retrieval with text generation. When someone asks a question, the application searches an external or private knowledge source, selects relevant material, and sends that material along with the question to a language model. The model then generates an answer conditioned on both.
This approach can ground answers in information that is specific to an organization or more current than the model’s training data. Updating the knowledge source and its index is usually more practical than retraining a model just to reflect a changed document. RAG does not, however, guarantee that the retrieved passages are relevant or that the model interprets them correctly. Microsoft’s RAG design and evaluation guide and AWS’s architecture overview describe the approach and its main stages.
RAG is not a vector database, a model, or a single plug-in. A working application also needs a way to ingest source data, prepare and index it, retrieve and rank passages, assemble prompts, call a model, and present the result. Depending on the use case, it may also need access controls, citations, monitoring, and user feedback.
How a RAG system works
RAG has two related flows: one prepares the knowledge before questions arrive, and the other uses that prepared index to answer each question.
1. Prepare and index the knowledge
- Connect to the sources. Collect the documents or other material the application is allowed to use, such as internal files or approved external content.
- Extract usable content. Convert source formats into text or other searchable representations. Extraction errors can make important facts unavailable even if they appear in the original file.
- Divide content into chunks. Break long sources into passages that preserve enough context to answer likely questions. The right boundaries depend on the source structure and task; there is no universally correct chunk size.
- Add metadata where useful. Attach details such as document identity, section, date, or access permissions. Metadata can support filtering and help preserve a path back to the original source.
- Create embeddings and store the index. An embedding model converts text into numerical representations that can be compared for semantic similarity. Store those representations in a searchable index, often alongside the text and metadata needed to return a usable passage.
Chunking approaches include fixed-size, sentence-based, custom, layout-aware, and machine-learning-assisted methods. Choose and test an approach against representative documents and questions rather than assuming a particular size or method will work for every corpus. Microsoft’s overview of RAG techniques discusses these approaches and how chunking affects retrieval.
2. Retrieve context and generate an answer
- Receive a question. The application accepts a user’s request and applies the relevant identity and access context.
- Search the index. A retriever finds candidate passages that match the question, subject to filters such as the user’s permissions.
- Select and assemble context. The system ranks candidates and chooses which passages fit the model call. Keeping each passage linked to its original source makes citations or verification possible.
- Call the language model. The application sends the question and selected context with instructions about how to answer, including what to do when the evidence is insufficient.
- Return and assess the answer. Present the response, and where appropriate its supporting sources. Evaluate whether the answer is relevant and supported by the retrieved material.
The ingestion work happens when the knowledge base is created or updated; retrieval and generation happen again for each question. A vector store is one common part of this design, not the whole system. AWS’s RAG architecture guidance outlines the broad flow and production components.
Do you need a vector database?
No. You need a way to find relevant information in the knowledge source; a vector database is one possible index, not a requirement for every RAG application. Vector search is useful for matching related meaning even when the query and document use different words. Full-text search can be better for exact terms, names, identifiers, or phrases. Hybrid search combines lexical and vector retrieval when both kinds of match matter.
| Retrieval method | What it searches for | When it may help |
|---|---|---|
| Full-text or lexical search | Words and terms that appear in the source text | Exact terminology, product codes, names, or phrases |
| Vector search | Semantic similarity between embeddings of the query and indexed content | Questions phrased differently from the source material |
| Hybrid search | A combination of lexical and vector signals | Queries where exact tokens and broader meaning both matter |
Which method performs best depends on the material and questions. Test retrieval against a representative set before choosing. Query rewriting can create alternative versions of a question before search; reranking can rescore initial candidates and pass a smaller, more relevant set to the model. Both add complexity, so use them when evaluation shows a need rather than treating them as mandatory RAG components. Microsoft’s technique guide covers full-text, vector, hybrid, rewriting, and reranking approaches.
Choose a retrieval pattern that fits the questions
Standard RAG for a direct question and one search
A standard pipeline follows a fixed sequence: receive a question, search an index, assemble context, and call the model. It is a practical baseline when a question can be answered with one search against one knowledge source. Its predictable flow is also easier to inspect and evaluate.
Agentic retrieval for multistep questions
In agentic retrieval, an agent can decide when to search, split a complex question into subqueries, or choose among sources at runtime. This can suit questions that require several searches or dynamic source selection, but it adds orchestration and makes the retrieval path less fixed. Microsoft recommends agentic retrieval for new implementations in its Azure AI Search documentation; that is a product-specific recommendation, not a universal rule for every RAG system. Microsoft’s Azure AI Search RAG overview describes both patterns.
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- Define the task and boundaries. Specify who will use the system, which sources it may search, what questions it should answer, and what it should do when the sources do not support an answer.
- Assemble representative data and test questions. Include realistic questions, questions that require different source types, and questions the available material cannot answer. Keep this set stable enough to compare changes.
- Build ingestion before tuning generation. Check that the system extracts the intended content, preserves meaningful sections, records useful metadata, and updates the index when its source material changes.
- Establish a simple retrieval baseline. Start with a standard pipeline and an appropriate search method. Add hybrid retrieval, rewriting, reranking, or agentic behavior only when the test results or task requirements justify them.
- Assemble context with provenance. Decide how many passages to pass to the model and preserve their source mappings if users need to verify answers. Instruct the model to rely on the supplied evidence and indicate when it is insufficient.
- Evaluate each stage and the complete answer. Test extraction and chunking, retrieval quality, and end-to-end responses separately. Track the configuration and aggregate results across the question set; a single compelling example is not enough to establish reliability.
- Secure and operate the whole flow. Apply permissions during retrieval, protect data in ingestion and storage, and monitor failures and changes to the source corpus. Re-evaluate after material changes to data, indexes, retrieval settings, or prompts.
Useful response-evaluation dimensions include groundedness, completeness, utilization of retrieved context, and relevancy. The right criteria and acceptable thresholds depend on the application; Microsoft’s design guide discusses evaluating both retrieval and generated responses.
How to diagnose weak RAG answers
When an answer is wrong or incomplete, locate the failure before changing the language model. A fluent answer can hide a retrieval problem, and a good passage can still be mishandled during prompt assembly or generation.
- The answer is absent from the retrieved context: check whether the source is present and current, whether extraction preserved the relevant content, and whether chunk boundaries or metadata filters exclude it.
- The retrieved passages are off-topic: inspect the search method and configuration, query wording, candidate ranking, and any access or metadata filters.
- The right passage appears, but the answer is unsupported: review which passages reach the model, how the prompt frames evidence and uncertainty, and whether the model is following those instructions.
- Results vary after an update: compare the source data, index, retrieval settings, and prompts with the recorded configuration from the earlier evaluation.
These checks distinguish data preparation, retrieval, and generation problems; changing the model alone may not address the actual cause.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Security and trust are part of the architecture
RAG does not make connected data safe to expose. Enforce access controls so users retrieve only material they are permitted to see, and apply metadata filters consistently. Protect the system at ingestion, storage, retrieval, and inference rather than treating the prompt as the only security boundary. Consider redaction at multiple stages where the data requires it.
Preserve source links or identifiers when users need to check claims. Guardrails can help manage accuracy, responsible use, and other risks, but they do not guarantee that an answer is correct or eliminate hallucinations and bias. AWS’s security guidance for generative AI discusses secure access and controls across the system.
Build it yourself or use a managed service?
A custom pipeline gives a team control over data processing, indexing, retrieval, and orchestration, but the team must also integrate and operate those parts. Managed services can reduce some implementation work, though their supported connectors, formats, controls, and retrieval options vary. Examples documented by their providers include Amazon Bedrock Knowledge Bases and Azure AI Search; these are implementation options, not evidence that one service is best for every workload. AWS documents how Amazon Bedrock Knowledge Bases work, while Microsoft documents RAG with Azure AI Search.
Before choosing, verify current capabilities against the actual requirements:
Quick Recap
- Which source systems and file formats can it ingest?
- How much control do you have over extraction, chunking, metadata, and index updates?
- Does it support the retrieval pattern you need, including hybrid or multistep retrieval?
- Can it apply your identity and access-control rules to retrieved passages?
- Can the application retain source provenance and inspect retrieval results?
- What monitoring and evaluation workflows are available, and what operational work remains yours?
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