RAG (Retrieval-Augmented Generation) lets AI systems answer questions based on your actual data, not just general internet knowledge. It is the architecture behind AI assistants that know your codebase, answer from your docs, or surface the exact policy a customer is asking about.
The problem RAG solves
Standard LLMs do not know your product or internal policies. Fine-tuning is expensive and goes stale fast. RAG retrieves relevant pieces at query time and injects them as context.
How it works simply
- Index: Your documents are chunked and converted into vector embeddings
- Retrieve: When a user asks a question, semantically similar chunks are found
- Generate: Those chunks are passed to the LLM with the question
Real-world use cases
- Customer support bots answering from your actual documentation
- Internal knowledge bases with natural language queries
- Legal tools surfacing relevant contract clauses
We have built RAG systems for clients in e-commerce, professional services and SaaS. Talk to us.