ai-engineer-expert

To build a RAG system, you ingest documents, generate embeddings, store them in a vector database, and execute similarity searches to retrieve context. This Skill provides Python code examples for the complete pipeline to query your documents.

Deploying LLM applications in production requires exposing FastAPI endpoints with rate limiting and circuit breakers to ensure stability. This provides example configurations to handle API traffic and prevent cascading failures during high-load scenarios.

The best way to structure prompts for LLM integration involves using templates for Chain-of-Thought, Few-Shot, and System Message generation. These techniques guide the model's reasoning and output format to achieve more predictable and accurate results.

Yes, you can build AI agents that use external tools to accomplish tasks. This provides a framework for agent development combined with LLM function calling capabilities, allowing the agent to interact with external APIs and execute complex workflows.

Yes, it supports a unified LLM client with fallback and streaming capabilities. This ensures your application maintains connectivity during provider outages and can process real-time token generation for interactive user experiences.

You need a vector database for RAG systems to retrieve external knowledge dynamically, whereas fine-tuning adjusts model weights for specific behavioral patterns or domain styles. This covers both model deployment strategies and embedding creation.