How do I use RAG for a private Q&A app over company files?

To build a private Q&A app over company files with RAG (Retrieval-Augmented Generation), the basic idea is:

1. Store company documents securely
2. Chunk and index them in a search system
3. Retrieve the most relevant chunks for each question
4. Send those chunks to an LLM to answer
5. Restrict access so users only see what they’re allowed to see

High-level architecture

User asks question
→ Auth / permissions check
→ Retrieve relevant document chunks
→ LLM answers using only retrieved context
→ Return answer + citations

Core components

1) Document ingestion

Collect files from:

• PDFs
• Word docs
• PowerPoints
• internal wikis
• emails / tickets, if allowed

Then:

• extract text
• clean it
• split into chunks
• attach metadata like:
  • file name
  • department
  • owner
  • creation date
  • ACLs / allowed users or groups

2) Indexing

Create embeddings for each chunk and store them in a vector database:

• pgvector
• Pinecone
• Weaviate
• Milvus
• Elasticsearch / OpenSearch vector search

Also store metadata for filtering:

• user/group permissions
• document type
• project
• sensitivity label

3) Retrieval

When a user asks a question:

• embed the query
• search for the top-k most relevant chunks
• filter by permissions
• optionally rerank results with a cross-encoder or LLM reranker

Good retrieval usually combines:

• vector similarity
• keyword search
• metadata filtering

4) Generation

Send the retrieved chunks to the LLM with instructions like:

• answer only from the provided context
• cite the source chunks
• say “I don’t know” if context is insufficient

5) Security and access control

For company files, this is critical.

Use:

• SSO / identity provider integration
• ACL-aware retrieval
• document-level and chunk-level permission checks
• encryption at rest and in transit
• audit logs
• data retention policies

Recommended pattern for private company Q&A

Ingestion pipeline

• parse file
• split into chunks
• inherit access permissions from source file
• generate embeddings
• store:
  • chunk text
  • embedding
  • metadata
  • ACL info

Query pipeline

1. authenticate user
2. determine user groups/roles
3. retrieve only chunks user can access
4. rerank top results
5. prompt LLM with retrieved context
6. return answer and citations

Prompting tips

Use a system prompt like:

You are a company assistant. Answer only using the provided context.
If the answer is not in the context, say you don’t have enough information.
Cite the source document and section for each claim.

Common mistakes

• No permission filtering: users may see data they shouldn’t
• Chunks too large or too small: hurts retrieval quality
• No citations: makes answers hard to trust
• Over-relying on the LLM: it may hallucinate
• Indexing everything blindly: sensitive docs need special handling
• No evaluation: you won’t know if retrieval is working

Good chunking practice

• aim for chunks of roughly 300–800 tokens
• preserve section headings
• keep tables or lists intact when possible
• add overlap between chunks
• store parent document references for citation

Evaluation

Test:

• retrieval accuracy
• answer faithfulness
• access control correctness
• latency
• user satisfaction

Use a small benchmark set of real company questions.

Minimal stack example

• Frontend: React / Next.js
• Backend: FastAPI / Node
• Auth: Okta / Azure AD / Auth0
• Embeddings: OpenAI / local embedding model
• Vector DB: pgvector or Pinecone
• LLM: GPT-style model or internal model
• Document parser: Apache Tika / unstructured.io

If you want a simple implementation plan

1. Choose file sources and auth system
2. Build ingestion and chunking
3. Add embedding + vector store
4. Implement ACL-aware retrieval
5. Add LLM answering with citations
6. Test on a small set of internal docs
7. Improve with reranking and evaluation

If you want, I can also give you:

• a reference architecture diagram
• a sample FastAPI/Node code skeleton
• or a secure design checklist for enterprise RAG