llm-prompt-engineering

Apply few-shot examples and chain-of-thought guidance within your LLM prompt to improve reasoning. Structured formats with clear task boundaries help the model follow complex logic and produce predictable outputs.

Structure system prompts using XML-like sections and explicit role assignment. This defines clear task boundaries for AI agents and enables easy programmatic parsing when integrating prompts with tooling.

Yes, structured prompt engineering works with GPT-4o, Claude, and Gemini. It applies model-specific best practices for system prompts and agent instructions to ensure consistent behavior across different large language models.

You need few-shot examples in prompt engineering when a task requires specific output formatting or reasoning patterns. Providing examples within structured prompts significantly improves model controllability and output reliability.

Use XML-like formats for LLM prompt templates to enable easy programmatic parsing and tooling integration. Structured sections separate instructions from data, improving reliability when processing outputs from large language models.