How do we implement RAG in our products?
RAG bridges the gap between data and dialogue.
How It Works:
Index your documents into a vector database, use embeddings to retrieve the top-k relevant chunks, then construct prompts that include those chunks for generation.
Key Benefits:
- Accuracy: Answers backed by source material.
- Maintainability: Update index when content changes.
- Customizability: Tailor retrieval strategies per domain.
Real-World Use Cases:
- Support knowledge base: Real-time, context-rich customer answers.
- Education tools: Generate lessons from curriculum documents.
FAQs
How choose k (chunks)?
How secure is the index?
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