RAG for Enterprise

Retrieval-Augmented Generation connects LLMs to your organization's knowledge base, enabling accurate, sourced answers instead of hallucinations. Enterprise RAG requires careful attention to chunking, retrieval quality, and security.

Why Enterprise RAG Is Different

Building RAG for a personal project versus an enterprise knowledge base involves fundamentally different challenges:

• Scale: Millions of documents across hundreds of sources, not a handful of PDFs
• Access control: Users should only see answers from documents they have permission to access
• Data freshness: Knowledge bases change constantly; embeddings must stay current
• Accuracy requirements: Enterprise decisions depend on correct information; hallucinations are costly
• Multi-format: PDFs, slides, spreadsheets, code, images, videos, and structured data

The Enterprise RAG Pipeline

1. Document Ingestion

   Connect to data sources (SharePoint, Confluence, Google Drive, databases) via connectors. Extract text from all formats using parsers.

2. Chunking

   Split documents into meaningful chunks. Use semantic chunking that respects document structure (headings, paragraphs, code blocks).

3. Embedding

   Convert chunks to vector embeddings using models optimized for retrieval (e.g., Voyage AI, Cohere Embed, OpenAI text-embedding-3-large).

4. Indexing

   Store embeddings in a vector database (Pinecone, Weaviate, Qdrant, pgvector) with metadata for filtering.

5. Retrieval

   At query time, embed the question and find the most relevant chunks using similarity search plus re-ranking.

6. Generation

   Pass retrieved chunks as context to an LLM with instructions to answer based only on the provided sources.

Chunking Strategies

Access Control in RAG

Enterprise RAG must enforce document-level permissions. A common approach:

# Store ACL metadata with each chunk
chunk_metadata = {
    "source": "confluence/engineering/arch-decisions",
    "allowed_groups": ["engineering", "leadership"],
    "classification": "internal",
    "last_updated": "2026-03-01"
}

# At query time, filter by user permissions
results = vector_db.query(
    query_embedding=embed(question),
    filter={"allowed_groups": {"$in": user.groups}},
    top_k=10
)

Improving Retrieval Quality

• Hybrid search: Combine vector similarity with BM25 keyword matching for better recall
• Re-ranking: Use a cross-encoder model to re-rank initial results for higher precision
• Query expansion: Use an LLM to rephrase the query or generate multiple search queries
• Contextual embeddings: Prepend document metadata (title, section) to chunks before embedding
• Parent document retrieval: Retrieve the full section or document around matching chunks