Prompt Engineering
patternThe practice of designing inputs to LLMs to reliably produce desired outputs.
Prompt engineering is the craft of writing instructions, examples, and context that guide an LLM toward useful behavior. It ranges from simple (clear instructions) to sophisticated (few-shot examples, chain-of-thought reasoning, structured output formats).
Key techniques include: system prompts (persistent instructions), few-shot examples (showing desired input/output pairs), chain-of-thought (asking the model to reason step by step), and output formatting (JSON, XML, or structured templates).
For agent systems, prompt engineering is critical. Each agent needs a system prompt that defines its persona, capabilities, and constraints. Tool descriptions must be precise enough for the model to use them correctly. The quality of prompts directly determines agent reliability.