prompt-engineering

To structure a system prompt for an LLM, use templates and best practices that define the AI's role, constraints, and expected output behavior to ensure controlled, reliable responses. This skill provides comprehensive guidance for designing and optimizing these foundational instructions.

The best way to get structured JSON output from a Large Language Model is to apply specific prompting techniques that enforce formatting rules and define tools for interaction. This skill guides generating specific formats like JSON to ensure structured data generation.

Chain-of-thought prompting improves generative AI reasoning by guiding the model through intermediate logical steps before reaching a final conclusion. This skill details this method, alongside few-shot examples, to significantly enhance complex reasoning and output accuracy.

To manage context window limits when building an AI chatbot, implement strategies for handling conversation history and large knowledge bases within token constraints. This skill provides techniques to effectively manage context windows and control sampling parameters.

You should use few-shot examples in your LLM prompts when you need to demonstrate specific output patterns or complex reasoning tasks to the model. This skill covers how few-shot examples guide the AI to produce desired outputs and improve behavior for specific tasks.