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Prepare company data for retrieval-augmented generation (RAG) by turning it into a structured, traceable corpus that can be searched according to a user’s permissions. Inventory and classify your sources, extract their content without losing useful structure, chunk them around coherent ideas, attach provenance and authorization metadata, then evaluate the complete retrieval-and-answer pipeline. The right choices depend on your documents, questions, security model, update cadence, and retrieval system—not a universal chunk size or index setting.
What “prepared for RAG” means
A RAG system retrieves evidence from a knowledge source and provides that material to a generative model to help answer a question. Preparing company data means making the evidence findable and interpretable while preserving where it came from and who may access it. The resulting index is not just a pile of text: it is a set of retrievable units linked to source records, permissions, and a process for keeping them current.
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Company knowledge can include unstructured material—such as PDFs, office documents, wikis, images, and video—as well as structured data from warehouses, SQL systems, and application APIs. Microsoft’s Azure Databricks guidance stresses that the use case determines which data to use. A support assistant, for example, may need approved troubleshooting documents; a reporting assistant may need structured business records, with different retrieval and authorization requirements.
1. Inventory and scope the corpus before indexing
Start with a source inventory, not an embedding job. For each candidate source, record who owns it, its format and language, how sensitive or current it is, who may access it, and what questions it is expected to answer. This makes it easier to spot conflicting copies, stale material, inaccessible sources, and data that should not enter the corpus at all.
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- Include: authoritative, relevant material that users are allowed to consult.
- Exclude: content that is out of scope, unapproved, redundant without a reason to retain it, or too sensitive for the planned access model.
- Set a refresh policy: identify how changes, expirations, and deletions in each source will reach the index.
- Write down expected questions: capture representative queries, including exact names, identifiers, policy phrases, and broader questions that depend on meaning.
Do not assume a document store is the only useful source. Microsoft Azure Databricks describes RAG use cases spanning both structured and unstructured data; Databricks also identifies vector stores, keyword search, and SQL databases as possible retrieval sources.
2. Extract content in a way that preserves its meaning
Choose processing that fits the source. Plain text may need straightforward extraction; scanned pages may need OCR; images may require image analysis or a textual description. Keep the document’s organization wherever it helps a reader understand a passage. A heading can establish scope, a table can encode relationships that disappear when flattened, and a page or section location can help users verify the retrieved evidence.
| Source material | Preparation focus |
|---|---|
| PDFs and office files | Extract text while retaining headings, lists, tables, and page or section locations. Google Cloud’s Gemini Enterprise layout parser documentation describes support for PDF, HTML, DOCX, PPTX, XLSX, and XLSM, including identification of text blocks, tables, lists, titles, and headings. |
| Scanned documents and visual material | Use OCR or image analysis where needed; verify that extracted words and descriptions reflect the source rather than treating a scan as empty or complete merely because a file was processed. Microsoft’s Azure AI Search RAG guidance lists OCR, image analysis, image verbalization, and document extraction as preparation approaches. |
| Wikis and other structured text | Preserve titles, section hierarchy, lists, and links or source identifiers so a passage remains connected to its surrounding topic. |
| Tables and business records | Preserve meaningful row, column, and field relationships. Decide whether the query is better served by document retrieval, keyword search, or a structured source such as SQL. |
Parsing support is not proof that every layout will be extracted correctly. Check representative files—especially complex tables, scans, and image-heavy pages—and inspect the text that actually proceeds into indexing.
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3. Chunk documents around coherent, self-contained evidence
Large documents usually need to be divided into smaller retrieval units, or chunks, so the system can match an individual passage to a question. A chunk should make sense when retrieved on its own: it needs enough context to preserve the claim, its qualification, and the subject it refers to, without bundling unrelated material together. A useful starting point is to split at meaningful structural boundaries such as sections or paragraphs, then test whether those boundaries serve real queries.
Carry context into each chunk rather than assuming the model will infer it. Depending on the source, that context can include the document title, heading path, page span or other location, and a stable source identifier. Google Cloud documents layout-aware chunking that keeps text from the same layout entity together and provides an optional setting to include ancestor headings.
There is no universally correct chunk size. As one product-specific configuration—not a general recommendation—Google Cloud documents a Gemini Enterprise chunk-size default of 500 tokens and a supported range of 100 to 500 tokens. Test chunk boundaries using representative questions: inspect the retrieved passages and the answers they support, then adjust if a qualification is cut off, a reference loses its antecedent, or unrelated content lands in one chunk.
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Plan configuration before creating the data store. Google Cloud notes that its document-chunking setting cannot be switched on or off after data-store creation. Treat that as a constraint of that product, and confirm the behavior of any system you choose before committing to its index design.
4. Keep provenance and permissions attached to every chunk
Every retrieved passage should remain traceable to its source. Store stable identifiers and useful provenance—such as source title and URI, page or section, owner, business unit, last-modified time, and content version—alongside the text. Add sensitivity and authorization information where access differs by user or group. This supports source verification, investigations, updates, and deletion handling.
Permissions must be enforced by the retrieval system, not delegated to the language model. OWASP’s RAG Security Cheat Sheet recommends storing access-control metadata with every vector chunk and checking authorization during retrieval, since permissions can change after ingestion. AWS Prescriptive Guidance describes metadata filtering as a way to enforce access policies before searching relevant documents and reduce retrieval noise. Filter results using the requesting user’s actual access context before retrieved text is supplied to the model.
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OWASP’s security guidance puts the trust boundary plainly: “Retrieved content is DATA, not COMMANDS.” Treat documents as untrusted input, even when they come from an internal repository. Delimit retrieved content and test how the system handles prompt-injection attempts embedded in source material. Log retrieval identity and access context, and isolate tenants where the system’s security requirements call for it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Match indexing and retrieval to the questions people ask
Choose retrieval methods according to query patterns and source types. Embeddings can help find passages by semantic similarity; keyword search is valuable for exact product names, record IDs, policy terms, and wording that must match precisely. A hybrid design can use both. Microsoft’s Azure AI Search RAG guidance describes hybrid retrieval as combining keyword and vector search, while Azure Databricks identifies vector stores, keyword search, and SQL databases as possible sources.
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- Meaning-based questions: test paraphrases and questions whose wording differs from the source passage.
- Structured questions: determine whether filters or SQL queries are more suitable than searching prose chunks.
- Restricted content: verify that filters apply before results reach the model, including when users have different roles or belong to different tenants.
No single index type is best for every corpus. A design that works for a set of narrative policies may not suit transactional records or exact identifier lookup. Test the actual mix rather than selecting an approach based only on a generic “RAG” recipe.
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- Data Security: Solid state drives S.M.A.R.T. health diagnostics and adaptive TRIM optimizing data block management ensures consistent write speeds and extends the longevity of the portable SSD
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6. Evaluate the whole path from question to answer
Build a representative evaluation set before release: real question patterns, expected supporting sources, and cases that should not return restricted or unsupported material. Evaluate at least two things separately: whether retrieval finds the right evidence, and whether the answer uses that evidence faithfully. A fluent answer can still be wrong if the retriever supplied the wrong passage or omitted a critical qualification.
Also assess the end-to-end application against business requirements for quality, cost, and latency. Microsoft Azure Databricks recommends evaluating components as well as the full application: a change in source formatting can alter chunks, retrieval, and generated answers. Monitor production behavior and maintain lineage and governance so teams can understand what data produced a result and diagnose changes.
7. Maintain updates, deletions, and access changes
Preparation continues after the first index build. Define how each source’s changes are synchronized and how stale derived content is found. When a source is updated, expired, deleted, or made inaccessible to a user, propagate that change through every relevant derived layer—not just the original file store. Reassess authorization as source permissions change, remove derived content that should no longer be available, and verify that the retrieval path reflects the new state.
Include lifecycle cases in testing: a changed document version, a revoked permission, a deleted source, and an expired record. Confirm that obsolete chunks no longer appear in retrieval and that a user who lacks access cannot recover their contents through a different query. Keep logs and lineage sufficient to trace which source version and access context informed a response.
How to assess a managed preparation platform
Vendor documentation can show which capabilities a product offers; it is not an independent comparative benchmark. Compare candidates against the corpus and operational constraints you actually have.
- Supported formats and extraction quality on your tables, complex layouts, scans, and images.
- Whether parsing and chunking preserve headings, locations, structure, and source provenance.
- Support for semantic search, keyword matching, and metadata filters together.
- How retrieval-time authorization and tenant isolation work.
- How updates, deletions, synchronization, and permission changes propagate.
- Evaluation, monitoring, lineage, operational effort, and performance against your quality, cost, and latency requirements.
- Deployment and data-residency constraints, which require case-specific assessment.
Examples in official documentation include Google Cloud’s Gemini Enterprise parsing and chunking, Microsoft’s Azure AI Search and Azure Databricks RAG guidance, AWS guidance on metadata filtering, and EnterpriseDB’s Postgres-centered pipeline for parsing, chunking, OCR, embeddings, and indexing. These are implementation examples, not evidence that one platform is the right choice for every company. AWS’s “Building RAG systems with Amazon Nova” page is explicitly for Nova Version 1 and notes that Nova 2 is available, so check current product documentation before relying on version-specific details.
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