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Retrieval-augmented generation (RAG) lets an AI answer using selected documents or other data: it searches for relevant passages, then gives those passages to a language model as context for its reply. The model can draw on information outside its training data, but the system still needs good source data, effective retrieval, evaluation and security controls.
What RAG means when you want to chat with your documents
RAG connects a language model to a knowledge source, such as company files, technical documentation or a curated collection of web pages. When someone asks a question, the application searches that source, selects passages that may help, and sends them to the model along with the question. The model then generates an answer using the supplied context. AWS describes this retrieve-and-supply approach, and Microsoft documents a similar design.
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Think of it as answering with an open book: the index helps find passages, and the model writes a response from the question and those passages. But finding text is not proof that it is relevant, complete or correctly interpreted. A RAG system can retrieve the wrong material, miss an important passage, or overstate what a source says.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →RAG supplies context at question time; it does not, by itself, change the model’s underlying training or guarantee that its answer is true. It is a way to make selected information available to the model when it responds.
#1 Best Overall
How a RAG system turns files into an answer
A typical implementation has a preparation stage and a question-answering stage. The first builds a searchable representation of the source material; the second uses it to find context for a particular query.
1. Prepare and index the information
- Connect sources and extract content. Bring in the documents or data the application is allowed to use, and process each file so its contents can be searched.
- Clean and divide the content. Remove or handle extraction artifacts, then split documents into chunks that preserve useful meaning. Chunks that are too small may lose context; chunks that are too large may make retrieval less focused.
- Add metadata. Attach useful details such as titles, keywords, source identifiers or access-control information. Metadata can help locate, filter and interpret chunks.
- Create embeddings and persist the index. An embedding represents a chunk in a form that supports semantic search. Store the searchable content, its representation and relevant metadata in an index or other retrieval system.
Microsoft’s design guide describes processing files, creating semantically useful chunks, adding metadata, generating embeddings and persisting results in a search index. The quality of the answers is therefore affected by choices made before anyone asks a question: extraction, cleaning, chunking, metadata and embedding configuration all influence what the system can find.
2. Retrieve context and generate a response
- Accept the user’s question. The application receives the query and applies the relevant orchestration and access checks.
- Search the index. The retrieval layer finds candidate passages, using a search strategy suited to the content and queries.
- Assemble context. The application selects and packages useful results with the question, often retaining source details for attribution.
- Call the language model. The model receives the combined prompt and generates a response. The application can then apply output checks and present the answer.
This is the core of “chat with my documents”: the model does not need to have memorized every document in advance. The application retrieves relevant material for each question and makes it available in that interaction. Production systems also need orchestration, user experience, guardrails and ongoing operations; retrieval and generation are only parts of the overall application.
Rank #2
Does RAG need a vector database?
No. Vector search is common, but it is not the only retrieval option. Microsoft’s design guidance considers full-text search, hybrid search and multiple searches as well as vector search. The right approach depends on the documents, the kinds of questions people ask and what evaluation shows.
| Retrieval approach | What it does | What to assess |
|---|---|---|
| Vector search | Uses embeddings to find content by semantic similarity. | Whether it retrieves relevant passages for representative questions, including varied wording. |
| Full-text search | Finds content through text-based search. | Whether it works well for the terms, names and phrases users actually search for. |
| Hybrid search | Combines search approaches. | Whether the combined results improve retrieval for the application’s query patterns. |
| Multiple searches | Runs more than one search as part of retrieval. | Whether the additional searches improve useful coverage enough to justify their complexity. |
The table describes categories, not a performance ranking: there is no universally best method established here. A vector index is one implementation choice, not a requirement inherent to RAG. Google Cloud’s reference architecture is one example of a vector-search implementation and points to database-backed and open-source alternatives; it should be read as a Google Cloud architecture, not a universal recommendation.
Fixed RAG pipeline or agentic retrieval?
A standard RAG pipeline follows a planned sequence: receive a query, search, assemble context and call the model. Microsoft says this pattern works well when a query maps to one search against one index. It is usually the clearer starting point when the task and information source are predictable.
Rank #3
Agentic RAG allows an agent to decide at runtime when to retrieve and which retrieval tools or sources to use. It can be appropriate when a task needs multiple reasoning steps, query decomposition, runtime source selection or retrieval combined with actions. That flexibility adds design and evaluation work: teams need to check not only the final answer, but also whether the agent chose appropriate steps and sources. Microsoft’s guide discusses standard and agentic approaches.
| Design | Useful when | Main consideration |
|---|---|---|
| Standard pipeline | A query can be handled with a designed sequence and a search against a known index. | Test whether that search reliably finds the context the task needs. |
| Agentic retrieval | A task may need several steps, runtime source choices, query decomposition or retrieval plus actions. | Evaluate the agent’s retrieval decisions as well as its generated response. |
How to improve RAG answer quality
Evaluate the complete path from source data to final response rather than judging a few impressive answers. Retrieval quality and generation quality are related, but distinct: a model cannot use evidence that was not retrieved, and retrieving a relevant passage does not ensure that the model uses it faithfully.
- Use representative questions. Include the kinds of queries people are expected to ask, not just examples that are easy for the system.
- Inspect what retrieval returns. Check whether results are relevant, sufficiently complete and drawn from the intended sources.
- Test the end-to-end answer. Assess groundedness, completeness, utilization of the supplied context and relevance to the question.
- Change one design choice at a time where practical. Chunking, metadata, embedding-model choice, index configuration and search type can all affect results.
- Record settings and aggregate results. Microsoft recommends documenting hyperparameters and evaluating across multiple queries, rather than treating one response as proof of quality.
The evaluation problem includes retrieval and generation, as well as factual accuracy, safety and efficiency. A 2025 survey by Aoran Gan, Hao Yu, Kai Zhang and coauthors reviews these dimensions in its survey of RAG evaluation. Use application-specific acceptance criteria: a system intended for internal policy questions may need different checks from one that answers technical support questions.
Does RAG prevent hallucinations?
No. RAG can give a model evidence to work from and may improve grounding, but it does not ensure that the retrieved evidence is relevant or that the model interprets it accurately. Poor, stale or incomplete source material can still lead to poor answers; so can retrieval failures and generation errors. Treat RAG as a way to provide information and testable evidence, not a truth guarantee. AWS’s explanation of RAG, Microsoft’s evaluation guidance and the 2025 evaluation survey all support treating system quality as dependent on more than retrieval alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to keep company data private and protected
Connecting private files to a model does not automatically make the resulting system private. Security needs to cover the full path: ingestion, storage, retrieval, generation and output. In particular, a search index must not let one user retrieve information they are not authorized to see.
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- Verify provenance and integrity. Establish where documents came from and protect against unauthorized or untrusted changes.
- Carry permissions into retrieval. Associate access-control metadata with every indexed chunk and enforce it when searching, rather than relying only on permissions at the original file location.
- Separate users and classifications. Design tenant and classification isolation so that retrieval cannot cross a boundary that should remain private.
- Control the index and connected systems. Vet data connectors, manage index access, and consider caches as well as the primary store.
- Validate and monitor outputs and operations. Apply output checks, logging and monitoring appropriate to the risks of the application.
- Define retention and deletion behavior. Plan how source updates and deletion requests propagate through indexes and related stored data.
- Fail closed when essential controls are missing. If a required permission or integrity check cannot be applied, do not silently proceed as though it succeeded.
These are among the measures in the OWASP RAG Security Cheat Sheet. The practical implication is that access control cannot be treated as an optional feature added after retrieval works: it shapes what the system is permitted to retrieve and show.
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How to compare RAG implementation options
Managed services and custom stacks are both possible. A managed design may reduce the work of operating some components; a custom design may offer more control over components and infrastructure. Neither is automatically the better choice. Compare options against the application’s actual requirements and test performance with representative data and questions.
- Sources and formats: Can the system connect to the relevant data and process the file types the organization uses?
- Refresh and re-indexing: How are changes to the source corpus reflected in the searchable content?
- Retrieval and data preparation: Can the implementation support the needed search methods, chunking, metadata and embedding choices?
- Permissions and isolation: Can it enforce access controls and separate tenants or classifications throughout retrieval?
- Evaluation and monitoring: Can teams inspect retrieval, assess generated answers and monitor operations?
- Operational fit: Does the balance of managed convenience and component-level control suit the team’s skills, infrastructure and governance?
- Deployment requirements: Check cost, latency, scale, geography and existing platform constraints against current product documentation before choosing.
For architectural examples rather than a provider ranking, see Microsoft’s guide to RAG design and evaluation and Google Cloud’s vector-search reference architecture. Capabilities and product names can change; verify current documentation for the specific services being considered.
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