llm-application-dev

To build a RAG pipeline, you implement document chunking and store the resulting embeddings to provide context for LLM queries. This Skill provides patterns for structuring retrieval systems and integrating them with AI applications.

The best way to structure prompts for consistent LLM chatbot responses is using structured, few-shot, and chain-of-thought prompt engineering techniques. This Skill provides patterns for crafting effective prompts to guide model behavior.

You integrate OpenAI and Anthropic APIs by applying specific code patterns for API interaction provided by this Skill. It includes examples for connecting to major LLM providers to power your AI applications.

You handle LLM API errors with retries and fallbacks by implementing robust error handling strategies for API calls. This Skill provides patterns to ensure resilient interactions when developing AI applications.

You need Retrieval-Augmented Generation when your LLM chatbot must answer user queries based on a specific knowledge base. This Skill covers building RAG systems to leverage document chunking and embedding storage for accurate responses.