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RAG (Retrieval-Augmented Generation) Systems
What is a Retrieval-Augmented Generation (RAG) system?
RAG is an architectural pattern that searches private database indices for relevant facts, injects them into the LLM system prompt, and asks the model to compile a response. This allows the model to reference proprietary files securely without retraining.
Key takeaways
RAG prevents LLM hallucinations by restricting answers to referenced search results.
Data remains secure since records are stored in private vector databases like Pinecone or pgvector.
ByteLeaps implements hybrid search (combining keyword BM25 and semantic vector embeddings) for accuracy.
Connecting LLMs to Your Private Databases
Fine-tuning models on business documents is expensive and static. RAG retrieves fresh, dynamic facts at query time, making it the preferred method for building question-answering systems on documents, databases, and customer records.