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To make AI answer questions about your documents, use retrieval-augmented generation (RAG): index your files, retrieve relevant passages for each question, and give those passages to a language model as evidence for its answer. A hosted file-search tool is the quickest route; a custom RAG pipeline gives you more control over parsing, retrieval, permissions, and storage.
How document question-answering works
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 searches indexed content for passages related to a question and supplies those passages to the model as context. Microsoft describes the process as two stages: indexing documents, then retrieving evidence and generating an answer for each query (Microsoft’s RAG overview).
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Indexing: prepare documents for search
A typical index pipeline extracts text, divides it into chunks, creates embeddings for those chunks, and stores the text and vectors with source metadata. Metadata can include the filename, page or section, document ID, and access-control information. OpenAI’s retrieval documentation explains how files in vector stores are processed for search (OpenAI Retrieval).
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Question time: find evidence, then generate
When someone asks a question, the system searches for relevant chunks and provides them to the model along with the question. Semantic search can find conceptually related passages even if they do not repeat the question’s exact wording. For names, codes, section numbers, or exact quotations, test keyword search or a hybrid approach as well.
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Choose a hosted file-search tool or a custom RAG pipeline
| Approach | What it does | Best fit | Trade-off |
|---|---|---|---|
| OpenAI File Search | A hosted Responses API tool 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. See OpenAI File Search. | Getting a file-search prototype working without assembling each retrieval component yourself. | Less direct control over pipeline details than a custom implementation. |
| Gemini File Search | Imports, chunks, and indexes data for retrieval and can return file-citation annotations. For paginated PDFs, annotations may include page numbers. Audio and video formats are not supported in the current documentation. See Google Gemini File Search. | A managed option when its documented file support and citation behavior meet your needs. | Check supported formats and citation details against your actual files. |
| Custom RAG pipeline | You choose the parser, chunking rules, embedding model, search store, ranking, and orchestration. Microsoft documents Azure patterns and combinations that include LangChain, LlamaIndex, or Haystack with Pinecone, Weaviate, or Qdrant. See Microsoft’s Azure RAG overview. | Requirements call for more control over data handling, integrations, search behavior, or evaluation. | You own more implementation, testing, and ongoing maintenance. |
| Claude Projects | Anthropic offers a no-code Projects option on paid Pro, Max, Team, and Enterprise plans. It automatically switches to RAG when project knowledge approaches or exceeds the context limit. See Anthropic’s RAG for Projects guide. | Asking questions about a grouped set of project files without building an application. | Plan eligibility and product behavior can change; follow Anthropic’s guidance on organizing project knowledge. |
There is no universal winner. Compare setup and maintenance effort, supported formats, citation needs, integrations, control over search and filters, data-location and retention requirements, expected ingestion and query costs, and how easily you can inspect failures. Vendor documentation describes product capabilities, not a neutral head-to-head accuracy or cost test.
Build a document-answering system step by step
- Define the corpus and permissions. Decide which files are in scope, who may search them, and how changed or deleted files will be reflected. Preserve authorization rules in metadata and enforce them in retrieval; do not assume that search automatically applies your organization’s access policy.
- Parse and normalize the files. Extract text and meaningful structure from each format. Check the output for scanned PDFs, tables, and complex page layouts; use OCR or layout-aware processing where needed. No single parser is established as reliable for every file and layout.
- Chunk content with source context. Split extracted text into retrievable pieces while keeping the filename and location information attached. Tune chunk size and overlap using representative questions; there is no universally optimal setting established by the cited vendor documentation.
- Create the index. Generate embeddings for the chunks and store the vectors alongside the chunk text and metadata. OpenAI vector stores handle chunking, embedding, and indexing for uploaded files; custom Azure patterns make these explicit pipeline steps (OpenAI Retrieval; Microsoft’s RAG overview).
- Retrieve passages for each question. Search the index for relevant chunks. Include keyword or hybrid retrieval in your evaluation if users need exact matches for identifiers, codes, or quoted language. OpenAI File Search documents semantic and keyword search (OpenAI File Search).
- Generate an evidence-grounded answer. Tell the model to answer from the supplied context, identify when the evidence is missing, and keep source references attached to supporting claims. Return citations that point to retrieved passages, not just citation-looking text generated by the model.
- Evaluate and improve the system. Assemble representative questions with expected answers and source locations. Check whether retrieval found the right evidence, whether the answer is supported by it, whether citations point to the right passages, and whether the system declines to guess when the index lacks an answer.
- Maintain the index. Re-index changed files, remove deleted material, and monitor ingestion failures. After updates, check that permissions and source references still work as intended.
Make answers traceable—and check that they are right
Carry source metadata from indexing through retrieval and into the answer display. A useful citation lets a reader open the source file and find the page, section, or passage supporting a claim. Microsoft’s RAG workflow describes retaining source metadata alongside indexed vectors, while Gemini File Search annotations can identify source files and may give page numbers for paginated PDFs (Microsoft; Google).
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A citation is a way to inspect evidence, not proof that an answer is correct. The retrieved passage could be irrelevant, incomplete, out of date, or misunderstood by the model. Test answers against the documents themselves, including cases where the right response is that the available files do not establish an answer.
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Common failure points to check
- The answer misses an important detail: inspect the extracted text and retrieved chunks first. A model cannot use evidence that parsing lost or search failed to retrieve.
- Exact names or codes are not found: try keyword or hybrid retrieval alongside semantic search.
- A PDF answer lacks useful location details: verify that parsing preserves page references and that the chosen tool exposes them. Gemini documentation says PDF annotations may include page numbers; this is not a guarantee for every file or response.
- A user can retrieve a restricted file: check how authorization metadata is applied to every search, not just how access is set in the original file system.
- A confident answer has weak support: review the retrieved passages and citation mapping, and test whether the system abstains when evidence is absent.
- Answers change after documents are edited: confirm the index is refreshed and that deleted or superseded content is no longer searchable.
Choose based on your files and constraints
Start with a managed tool if you want to validate the experience quickly and its file support, data handling, and citation behavior meet your needs. Choose a custom pipeline when you need tighter control over parsing, chunking, ranking, permissions, storage, or integration. In either case, decide with a trial on your own documents and realistic questions: the cited product pages do not establish a universal accuracy or cost winner.
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