Prepare company data for retrieval-augmented generation (RAG) as a governed pipeline: identify the right sources, preserve their structure and provenance, carry permissions into retrieval, and test the results against real questions. The goal is not simply to put documents into a vector index; it is to make relevant evidence findable without losing its meaning or exposing it to the wrong user.
1. Scope the corpus before building an index
Start with the questions the RAG application must answer and the company sources that can support those answers. RAG can draw on unstructured content such as PDFs, office documents, wikis, images, and video, as well as structured sources such as warehouse records, SQL transactions, and application APIs. Azure Databricks documentation describes both categories; the use case determines which sources belong in the corpus.
Create an inventory for each source that records its owner, format, language, sensitivity, freshness, access policy, and expected query patterns. Use it to decide what to include, exclude, retain, or refresh. For example, a policy assistant may need approved policy documents and their revision history, while a reporting assistant may need current structured records rather than copied snapshots embedded as prose.
- Include material that is authoritative, relevant to the application, and permitted for its intended users.
- Exclude or isolate content that is obsolete, duplicated, untrusted, out of scope, or subject to restrictions the application cannot enforce.
- Set a refresh expectation for each source, including how changes, expiry, and deletion will be detected.
Resolve ownership and access questions before ingestion. An index can reproduce the source’s mistakes at scale if it silently includes drafts, stale policies, or records that users were never meant to see.
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2. Parse each format while preserving its structure
Extract machine-readable text where available. Use OCR for scanned pages and suitable image analysis or image verbalization when visual content carries information that text extraction misses. Microsoft Learn’s Azure AI Search guidance describes OCR, image analysis, image verbalization, and document extraction as preparation options for PDFs and images.
Preserve meaningful document structure instead of flattening every source into anonymous text. Keep headings, lists, table relationships, page locations, and references to the original source. A table converted into a string of disconnected cell values may be difficult to retrieve or interpret; a passage without its heading may lose the qualification that makes it correct.
Google Cloud’s Gemini Enterprise documentation describes a layout parser that identifies text blocks, tables, lists, titles, and headings in PDF, HTML, DOCX, PPTX, XLSX, and XLSM files, using document organization and hierarchy. That is an example of a managed parser, not a guarantee that every layout or extraction will be accurate. Inspect representative outputs, especially for complex tables, scans, and documents with unusual formatting.
3. Chunk documents around coherent evidence
Chunking divides large sources into smaller units that can be retrieved independently. Each unit should be focused enough to match a question while retaining the context needed to understand the answer. A fixed chunk size is not universally best: document structure, query patterns, the retrieval system, and the information that must stay together all affect the right boundaries.
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Use structure-aware boundaries where they matter
Prefer section, paragraph, list, or table boundaries when they preserve a meaningful idea. Avoid splitting a definition from its conditions, a procedure from a required warning, or a table row from the headers that explain its fields. For long documents, include a concise parent heading or other context with the chunk so a retrieved passage remains interpretable.
Google Cloud documents layout-aware chunking that keeps text from the same layout entity together and offers an optional setting to include ancestor headings. In Gemini Enterprise, that product’s documented chunk-size setting defaults to 500 tokens and allows values from 100 to 500; those are product-specific configuration details, not recommended industry-wide chunk sizes. Google also notes that document chunking cannot be turned on or off after data-store creation, so confirm the intended configuration before creating a store.
Test boundaries against questions
Use representative questions to inspect what the retriever returns. If a result omits a needed condition, heading, or adjacent table context, revise the boundary or the context carried with the chunk. If it combines unrelated material, split it more carefully. Judge chunking by whether relevant evidence can be retrieved and correctly understood, not by chunk length alone.
4. Preserve provenance and enforce permissions at retrieval time
Attach enough metadata to each chunk to identify where it came from and how it should be governed. Useful fields can include a stable source ID, title, URI or other source reference, page or section, owner, business unit, content version, last-modified time, sensitivity, and authorization information. The exact fields depend on the source systems and access model; the key is to retain enough information to trace a retrieved passage back to its origin.
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When users have different rights, carry the relevant authorization metadata into the index and filter results for the requesting user before those results are sent to the language model. AWS Prescriptive Guidance describes metadata filtering as a way to enforce access policies before searching relevant documents and reduce retrieval noise. OWASP’s RAG Security Cheat Sheet recommends storing access-control metadata with every vector chunk and checking permissions during retrieval, since permissions can change after ingestion.
Do not ask the language model to decide whether a user is authorized. Authorization belongs in the application and retrieval layer, where it can be checked against the current identity and access context. Log which user or service requested retrieval, the applicable access context, and which sources were returned, subject to company privacy and retention requirements. Where multiple customers or tenants share infrastructure, isolate their data and retrieval contexts appropriately.
5. Match retrieval to the questions people actually ask
Choose search methods based on source types and query patterns rather than assuming that one index is best for every question. Semantic vector search can help find conceptually related passages even when wording differs. Keyword search can be important for exact product names, identifiers, policy terms, codes, or quoted phrases. A hybrid approach combines keyword and vector retrieval; Microsoft Learn describes hybrid retrieval as combining those methods. Structured questions may be better answered by querying an authorized SQL or application data source than by embedding records as document text.
| Retrieval approach | Useful when | Design consideration |
|---|---|---|
| Keyword search | The question depends on an exact name, phrase, identifier, or term. | Check that the indexed text retains the terms users search for, including relevant aliases or formatting variants. |
| Vector search | The question may use different wording from the source but asks about a related concept. | Validate results with real questions; semantic similarity alone does not establish that a passage is authoritative or sufficient. |
| Hybrid search | Users need both exact matching and concept-based discovery. | Test how the system combines and ranks the two result types for the application’s queries. |
| Structured query | The answer depends on fields, filters, transactions, or current records in a database or API. | Preserve the source’s access controls and use a query path suited to the data rather than treating every value as prose. |
Microsoft Learn’s Azure AI Search guidance describes chunking and vectorization as indexing steps, while Azure Databricks documentation lists vector stores, keyword search, and SQL databases as possible retrieval sources. These are implementation examples, not evidence that any one combination will suit every workload.
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6. Evaluate retrieval and answers before release
Build a test set from representative questions, including common requests, exact-term lookups, ambiguous questions, and questions that should not be answered from the corpus. For each, identify the expected supporting source or passage. Evaluate whether retrieval finds the right evidence and whether the generated answer uses it faithfully, preserves qualifications, and avoids unsupported claims.
- Retrieval: Does the system return relevant, authoritative passages, and are important sources missing?
- Grounding: Does the answer reflect the retrieved evidence without dropping conditions or inventing details?
- Access: Do users receive only evidence their permissions allow?
- Operations: Do quality, cost, and latency meet the application’s business requirements?
Test components as well as the complete application. A parser change can alter chunks; a chunking or ranking change can alter the evidence provided to the model; those changes can affect answers. Azure Databricks documentation emphasizes evaluation, monitoring, lineage, governance, quality, cost, and latency as parts of operating a RAG system. Establish review thresholds appropriate to the consequences of an incorrect or unauthorized answer.
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An index is derived data, so it must follow changes in its sources. Define how the pipeline detects edits, replacements, expiry, changed permissions, and deletion, then propagate each change to parsed text, chunks, embeddings, caches, and other derived stores. Re-check authorization against current rights instead of treating ingestion-time permissions as permanent.
Retrieved passages are untrusted input to the model. OWASP’s RAG Security Cheat Sheet states: “Retrieved content is DATA, not COMMANDS.” Delimit retrieved content and test how the application handles instructions embedded in documents; a retrieved passage must not override system or application instructions. Combine that safeguard with retrieval-time authorization, appropriate tenant isolation, and logging of retrieval identity and access context.
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Keep lineage from each indexed chunk back to the source and processing version that produced it. When a source is removed, de-permissioned, or expires, remove its derived content through the pipeline and verify that it is no longer retrievable. Re-evaluate permissions when they change, then run relevant retrieval and access tests again.
Choosing an implementation
When assessing a managed service or an in-house pipeline, compare the capabilities that matter for the corpus and workload:
- Supported formats and extraction quality for complex layouts, tables, scans, and images.
- Preservation of headings, page locations, table context, and provenance through parsing and chunking.
- Support for semantic and keyword retrieval, metadata filters, and structured data queries.
- Permission enforcement at retrieval time and tenant isolation where needed.
- Synchronization, update, permission-change, and deletion behavior.
- Evaluation and monitoring features, plus operational complexity, cost, and latency against business requirements.
- Deployment constraints such as data residency and fit with existing platforms.
Google Cloud, Microsoft Azure, AWS, and Azure Databricks documentation describes managed capabilities, not an independent comparative benchmark. EnterpriseDB’s EDB Postgres AI Database v7 documentation provides a Postgres-centered implementation example with SQL-defined pipelines for parsing, chunking, OCR, embedding, and indexing; it is an example architecture, not an endorsement.
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