rag

What it does

This skill enables AI agents to retrieve and utilize information from external data sources, allowing them to answer questions based on specific documents or datasets rather than just their training knowledge. It bridges the gap between general model capabilities and domain-specific information by managing retrieval-augmented generation workflows.

When to use it

• You need an agent to analyze custom company policies, legal contracts, or internal wikis that aren't part of its base training data.
• Your application requires dynamic answers based on a specific knowledge base that changes frequently, such as real-time inventory logs or support tickets.
• You want to reduce hallucinations by grounding the AI's responses in verified, retrieved context from provided files.

Key capabilities

• Fetches relevant text chunks from external documents based on user queries.
• Integrates with various vector databases and search engines for efficient data retrieval.
• Formats retrieved context to enhance the accuracy of subsequent LLM generations.
• Supports multi-document analysis by aggregating information across several sources.

Example prompts

• "Summarize the Q3 financial report using only the attached PDF documents."
• "What are the current return policies for products listed in this spreadsheet?"
• "Explain the incident response procedure based on the security manual I uploaded."

Tips & gotchas

Ensure your external data sources are properly indexed and cleaned before relying on retrieval accuracy. Complex queries may require tuning the chunk size or embedding model to match specific document structures effectively.