How to Build a RAG System for Company Documents
- Author
- Vishal Maurya
- Published on
- Reading time
- 2 min read
Overview
A retrieval-augmented generation system can answer questions using company documents, but retrieval quality and permissions usually matter more than the prompt alone. Poorly extracted tables, stale indexes, or missing document-level access controls can produce confidently wrong or unauthorized answers.
Start with document types, volume, update frequency, access rules, and the questions employees need answered. Choose extraction and chunking strategies based on the content; a policy PDF, spreadsheet, scanned contract, and technical manual do not all need the same parser.
Design the retrieval pipeline
- Extract text and tables while preserving document IDs, page numbers, and metadata.
- Normalize and chunk content according to headings, tables, and semantic boundaries.
- Generate embeddings and index metadata needed for filtering and updates.
- Apply user and document permissions before returning chunks to the model.
- Generate answers with citations that point back to retrieved source locations.
- Evaluate retrieval recall, citation correctness, refusal behavior, and answer quality on real questions.
Example: a concrete starting point
def build_context(chunks, max_chars: int = 12_000) -> str:
selected = []
size = 0
for chunk in chunks:
text = chunk.text.strip()
if not text:
continue
if size + len(text) > max_chars:
break
selected.append(f"[source={chunk.source_id}, page={chunk.page}]\n{text}")
size += len(text)
return "\n\n".join(selected)
The example illustrates the boundary and validation pattern; adapt names, persistence, error handling, and provider-specific details to your application. It is not a complete production system by itself.
Failure cases to test
Do not retrieve all documents and filter permissions only in the prompt. Treat document text as untrusted input. Keep a re-indexing strategy for changed files and delete chunks when source documents are removed or access is revoked.
Before release, test the happy path, invalid input, missing permissions, dependency failure, and a retry or duplicate request where relevant. Record expected behavior so the implementation can be reviewed against the real business requirement.
When to bring in a developer
I can build a document Q&A system that handles PDF extraction, access-aware retrieval, citations, and practical evaluation.
Conclusion
The reliable version of this feature is not just the happy-path demo. It makes ownership, validation, authorization, failure handling, and operational behavior explicit. Start with the smallest useful workflow, measure it against real data, and expand only when the next requirement is clear.