Retrieval Augmented Generation (RAG) allows AI agents to search and use your own data. Here is how we implement RAG pipelines for clients.
What is RAG?
Large Language Models like GPT-4o and Claude are powerful but they do not know anything about your specific business, documents, or data. RAG solves this by giving the AI a way to search your private knowledge base before answering.
How It Works
1. Your documents are chunked and converted into vector embeddings using an embedding model. 2. These embeddings are stored in a vector database (we use Pinecone or pgvector). 3. When a user asks a question, the question is also embedded and the most similar document chunks are retrieved. 4. These chunks are injected into the AI's context window along with the question. 5. The AI answers using your real data.
When to Use RAG
RAG is ideal for customer support bots trained on your documentation, internal knowledge base assistants, legal document Q&A tools, and any scenario where the AI needs to reference your specific business knowledge.
Our Implementation
We typically use Laravel + Python FastAPI for the RAG backend, Pinecone for vector storage, and either Claude or GPT-4o as the LLM. The full pipeline can be built and deployed in 2–4 weeks.