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
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RAG prevents LLM hallucinations by restricting answers to referenced search results.
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Data remains secure since records are stored in private vector databases like Pinecone or pgvector.
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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.
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