Rag Engineer

What it does

The rag-engineer skill allows AI agents to optimize Retrieval Augmented Generation (RAG) pipelines. It can automatically evaluate RAG performance, identify bottlenecks in the retrieval process, and suggest improvements to enhance accuracy and relevance of generated responses. This includes adjusting chunk sizes, embedding models, and retrieval strategies.

When to use it

• Improving RAG Accuracy: When a knowledge base is used for context but the AI's answers are often inaccurate or irrelevant.
• Optimizing Retrieval Speed: To reduce latency in RAG pipelines where response time is critical.
• Evaluating Different Knowledge Bases: To compare the effectiveness of various data sources when building a RAG system.
• Tuning Embedding Models: When exploring different embedding models to find the best fit for your specific knowledge base and query patterns.

Key capabilities

• RAG performance evaluation
• Bottleneck identification in retrieval process
• Chunk size optimization
• Embedding model selection assistance
• Retrieval strategy adjustment

Example prompts

• "Evaluate the RAG pipeline's accuracy using this dataset."
• "Suggest improvements to my RAG setup; I’m seeing slow response times."
• "Compare the performance of Sentence Transformers and OpenAI embeddings for my knowledge base."

Tips & gotchas

The skill requires access to both your knowledge base data and a mechanism for evaluating generated responses. Ensure you have appropriate permissions and logging in place before running evaluations.