Stop trying to train LLMs on your documents. Teach them how to read your documents instead.
The Hallucination Problem
Enterprises cannot afford AI that makes things up. Fine-tuning a model injects knowledge into its weights, but it is imprecise and hard to update. If your policy changes tomorrow, you have to re-train.
Enter RAG
Retrieval-Augmented Generation decouples knowledge from reasoning. The architecture is:
- Ingest: Chunk and embed documents into a Vector Database key.
- Retrieve: When a user asks a question, search for relevant chunks.
- Generate: Pass the chunks to the LLM as context instructions.
Vector Databases: The New SQL?
The rise of tools like Pinecone, Weaviate, and pgvector has made semantic search accessible. We are seeing a massive shift where "search" is no longer keyword matching, but meaning matching.
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