llm-application-dev

AI application development integrates OpenAI and Anthropic APIs to build chatbots and automate tasks. This Skill provides API integration patterns, enabling direct communication with large language models for generating responses and automating workflows.

Implementing RAG patterns enhances LLM responses by retrieving external documents to ground the generated output. This Skill provides a basic RAG pipeline to augment large language model context, improving accuracy and contextual relevance.

Prompt engineering techniques are required for structured AI interactions to achieve effective outputs. This Skill offers few-shot examples and chain of thought techniques, guiding large language models to process complex queries accurately.

Handling errors and retries when calling large language models prevents application crashes during API timeouts. This Skill implements a safe LLM call function that manages automatic retries and fallbacks for robust AI application development.

Token management limitations in LLM applications include context window constraints and associated API costs. This Skill offers best practices for caching, evaluation, and privacy considerations to optimize token usage and maintain data security.