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To make AI answer questions about your documents, use retrieval-augmented generation (RAG): index the files, retrieve relevant passages for each question, and give those passages to an AI model as evidence for its answer. For a quick start, a hosted file-search tool handles much of the indexing and retrieval. For more control over parsing, permissions, search, and storage, build a custom RAG pipeline.
What happens when AI answers from your documents?
RAG connects a language model to an external collection of documents at answer time. It does not retrain the model on your files. Instead, the system finds relevant content and supplies it as context for the model’s response. Microsoft describes the process as two stages: indexing documents ahead of time, then retrieving matching content when someone asks a question.
- Indexing: Parse documents, divide their content into retrievable chunks, create embeddings, and store the chunks and associated metadata.
- Question answering: Search the indexed content for passages relevant to the question, then send those passages and the question to a model to generate an answer.
Both stages matter. If important text was missed or poorly represented during indexing, the search may not find it. If retrieval finds the wrong passages, a fluent answer can still be wrong.
Choose a managed tool or a custom pipeline
Hosted tools reduce the amount of retrieval infrastructure you need to build. A custom pipeline exposes more of that infrastructure for you to configure. Neither approach is established as universally more accurate or cheaper; choose by testing your own files, questions, integrations, and operational requirements.
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| Approach | What it does | Best fit | What to consider |
|---|---|---|---|
| OpenAI File Search | A hosted Responses API tool that searches files in vector stores using semantic and keyword search. Create a vector store and upload files before using it; retrieval is handled when the model invokes the tool. OpenAI File Search documentation | Teams building with the OpenAI API that want a managed file-search path. | Review file support, data requirements, cost at expected usage, and the degree of control you need over parsing and retrieval. |
| Google Gemini File Search | Imports, chunks, and indexes files for retrieval; responses can include file-citation annotations and may expose page numbers for paginated PDFs. The current documentation says audio and video formats are not supported. Gemini File Search documentation | Teams building with the Gemini API that want a managed indexing and citation path. | Check supported formats and how citations appear in the response for your use case. |
| Claude Projects | A no-code option for paid Pro, Max, Team, and Enterprise plans. Anthropic says Projects automatically switches to RAG when project knowledge approaches or exceeds the context limit. Anthropic’s Projects RAG guidance | People who want to ask questions about project files without building an API-based system. | Eligibility and product behavior can change. Anthropic recommends comprehensive content, descriptive filenames, related-file grouping, and naming specific documents in questions. |
| Custom RAG pipeline | You select and connect the parser, chunking approach, embeddings, search store, filters, and model orchestration. Microsoft documents Azure patterns and combinations involving LangChain, LlamaIndex, or Haystack with Pinecone, Weaviate, or Qdrant. Microsoft’s Azure RAG overview | Teams that need more control over data flow, retrieval behavior, permissions, or integration. | Plan for building, evaluating, debugging, and maintaining the pipeline and its index. |
When comparing options, consider setup and ongoing effort, file and citation support, parsing and ranking controls, identity and cloud integrations, data-location and retention requirements, expected ingestion and query volume, and how easily you can inspect retrieval results.
How to build document Q&A
1. Define the document collection and access rules
Decide which files belong in the collection, how updates and deletions will reach the index, and which users may search each file. Carry access-control information into metadata and apply it as retrieval filters. Do not assume that a search tool automatically enforces your organization’s authorization rules.
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2. Extract text and preserve useful structure
Parse the formats in your collection and retain meaningful details such as headings, page boundaries, and table content where possible. Check the extracted text against the original, especially for scanned PDFs or documents with complex layouts. OCR or layout-aware processing may be needed; no universal parser is established as equally effective across every file type and layout.
3. Split content into chunks with source context
Divide the extracted content into passages small enough to retrieve usefully, but preserve enough surrounding context for the passage to make sense. Store each chunk with metadata such as its document ID, filename, page or section, and relevant permissions. Chunk size and overlap should be tuned using representative questions; there is no universally optimal value established by the cited sources.
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4. Index the chunks
Create embeddings for the chunks and store them with the chunk text and metadata. Embeddings represent content in a form that supports semantic matching. With a custom system, this is an explicit pipeline step; OpenAI’s vector-store workflow can automatically chunk, embed, and index uploaded files. OpenAI Retrieval documentation
5. Retrieve evidence for each question
At question time, search the index and select relevant passages for the model’s context. Semantic search can find related ideas even when the question and document do not use identical terms. If users ask about exact identifiers, product codes, section numbers, or quoted wording, test keyword or hybrid search too. OpenAI File Search combines semantic and keyword search. OpenAI File Search documentation
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6. Ask the model to answer from the evidence
Provide the retrieved passages with the question and instruct the model to use only evidence those passages support. Tell it to identify missing information rather than fill gaps with a guess. Preserve source references through the answer process so the interface can show citations linked to the retrieved passages.
7. Test retrieval, answers, and citations
Create a representative set of questions, expected answers, and expected source locations from the documents people will actually use. Check each part separately:
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- Retrieval: Did the system find the passage that contains the answer?
- Answer support: Does the response follow from the retrieved evidence, without adding unsupported claims?
- Citations: Do the references lead to the passages that support the claims?
- Abstention: Does the system say when its available documents do not establish an answer?
Vendor documentation describes product features, not a neutral benchmark establishing a general accuracy level for document-answering systems. Your own evaluation is necessary to determine whether an approach works for your collection and queries.
8. Keep the index in sync
Re-index changed files and remove deleted ones. Track ingestion failures and check that access filters and source references still work after updates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to get useful citations
Citations depend on preserving the connection between a response and its source. Store document identifiers and location metadata during indexing, carry them through retrieval, and render the matching references in the answer. Microsoft’s RAG guidance describes storing source metadata alongside indexed vectors. Microsoft’s Azure RAG overview
Gemini File Search can return source-file citation annotations and may include page numbers for paginated PDFs. Gemini File Search documentation A citation makes it easier to inspect the cited material, but it is not proof that the answer is correct: the passage may be irrelevant, incomplete, out of date, or misunderstood.
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Quick Recap
Common problems and what to check
- The answer misses a fact that is in a file: Check whether extraction captured the relevant text and whether the search returned its passage. For scanned pages or complex tables, validate OCR or layout processing.
- The system misses an exact term or identifier: Test keyword or hybrid retrieval alongside semantic search.
- The answer sounds certain but is unsupported: Inspect the retrieved passages and tighten the instruction to distinguish evidence from missing information. Test whether the system abstains when no supporting passage exists.
- A citation points to the wrong place: Check that filenames and page or section metadata were retained and attached to the retrieved chunk.
- A user sees documents they should not access: Review the authorization rules and verify that permissions are applied as retrieval filters, not merely stored as unused metadata.
- Answers use stale material: Check update and deletion handling and confirm that the index reflects the current files.
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