Retrieval Augmented Generation (RAG) is a powerful technique that enhances AI models by providing them with relevant information from your own data. Instead of relying solely on what an AI was trained on, RAG lets you ground responses in your specific documents, knowledge bases, and data.
Add metadata to chunks: Our chunk editor lets you add custom metadata to document chunks, improving retrieval accuracy by providing additional context about the source material and helping the LLM understand the relevance of information.
Create focused collections: Group related documents together in separate collections to improve search relevance and reduce noise when retrieving information for specific domains or topics.
Experiment with chunk sizes: Smaller chunks work better for specific questions while larger chunks provide more context. Try different sizes (150-500 tokens) based on your specific use case and document types.
Choose the right embedding model: Different models have different strengths and context lengths. Match your embedding model to your content type - specialized models often outperform general ones for domain-specific content.
Provide clear instructions to your LLM: Tell it explicitly to use the retrieved context, cite sources when possible, and acknowledge when information might be missing from the provided context.