What is RAG and Why It Matters
Retrieval-Augmented Generation (RAG) is an architecture pattern that combines the retrieval power of search with the synthesis capability of large language models. Instead of fine-tuning an LLM on your data (expensive, slow, and hard to update), RAG retrieves relevant information at query time and feeds it to the LLM as context.
The result: an AI system that answers questions about your data with source citations, updates in real-time as documents change, and respects access controls — without training a custom model.
RAG is not a product you install. It’s an architecture that must be engineered for your specific data, use cases, scale, and security requirements. The difference between a demo RAG system and a production one is significant.
Our RAG Architecture
Ingestion Pipeline
Your data lives in many places — Confluence, SharePoint, Google Drive, internal wikis, databases, email archives, Slack. Our ingestion pipeline connects to all of these, extracting text, metadata, and document structure.
Documents are chunked intelligently — not arbitrarily split at 500-token boundaries, but segmented at natural boundaries (sections, paragraphs, logical units) that preserve context. Chunk metadata captures source, section hierarchy, creation date, and access permissions.
The pipeline runs continuously, detecting new and modified documents and re-indexing them incrementally. You don’t rebuild the entire index when one document changes.
Embedding & Vector Storage
Each chunk is converted into a vector embedding — a numerical representation that captures semantic meaning. We select embedding models based on your requirements: multilingual capability, domain specificity, and latency constraints.
Vectors are stored in a purpose-built vector database (Pinecone, Weaviate, or pgvector depending on scale and deployment model). The database enables fast similarity search — finding the passages most semantically related to a user’s question, even when the wording differs completely.
Retrieval Strategy
Simple vector similarity search is a starting point, not the final answer. Production RAG systems use hybrid retrieval combining:
- Semantic search — vector similarity for meaning-based matching
- Keyword search — BM25 for exact terms, names, codes, and identifiers
- Metadata filtering — restrict results by date, source, document type, or access level
- Re-ranking — a secondary model that scores and reorders retrieved passages for relevance
This multi-stage retrieval dramatically improves answer quality compared to naive vector search alone.
Generation With Grounding
The retrieved passages are assembled into a context window and fed to an LLM along with the user’s question. The prompt engineering is critical here:
- The LLM is instructed to answer only from the provided context
- Every claim must be tied to a specific source passage
- If the context doesn’t contain enough information to answer, the system says “I don’t have enough information to answer this” rather than guessing
- Answers include citations linking back to source documents
This grounding approach dramatically reduces hallucination compared to using an LLM alone.
Evaluation & Quality Assurance
We don’t deploy and hope. Every RAG system includes:
- Automated evaluation against curated question-answer test sets
- Retrieval quality metrics — are the right passages being retrieved?
- Answer quality metrics — are answers accurate, complete, and well-cited?
- Hallucination detection — does the answer contain claims not supported by retrieved context?
- User feedback loops — thumbs up/down on answers feeds back into retrieval tuning
Access Control & Data Security
Enterprise knowledge systems must respect existing permission structures. Our implementation enforces access controls at the retrieval level:
- Documents are tagged with access permissions during ingestion
- User queries are filtered to only retrieve documents the user is authorized to access
- The LLM never sees content the user shouldn’t access
- All queries and responses are logged for audit purposes
For organizations with strict data sovereignty requirements, we deploy entirely self-hosted — vector database, embedding models, and LLMs running within your infrastructure. No data leaves your environment.
Common Pitfalls We Avoid
Many RAG implementations fail in production because they skip the engineering fundamentals:
Poor chunking — arbitrary token limits that split mid-sentence or mid-paragraph, destroying context. We chunk at semantic boundaries.
No hybrid retrieval — relying solely on vector search misses exact matches for names, codes, and technical terms. We combine semantic and keyword search.
Missing evaluation — no way to know if answer quality is degrading over time. We build automated evaluation into every deployment.
Ignoring metadata — treating all documents as equal regardless of recency, authority, or relevance. We use metadata filtering and boosting.
No access controls — a knowledge system that leaks confidential information is worse than no system at all. We enforce permissions at the retrieval layer.
These are engineering problems, not model problems. The LLM is the easy part. The retrieval pipeline, evaluation framework, and access control layer are what make it production-ready.