Prompt Engineering
The practice of crafting effective inputs to AI models to produce desired outputs.
Prompt Engineering is the discipline of designing inputs (prompts) to AI models to elicit better outputs. Techniques include clear task framing, example-driven prompts (few-shot learning), chain-of-thought prompting, role assignment, and structured output formatting.
Production LLM applications often have sophisticated prompt engineering layered with system messages, dynamic context injection, and output validation. Treating prompts as code (versioned, tested, monitored) separates serious LLM apps from one-off experiments.
A team improves their support assistant by replacing 'Answer the user's question' with a multi-paragraph system prompt covering tone, escalation rules, response format, and three example interactions. Response quality and consistency dramatically improve.
Related terms
A neural network trained on vast text data to understand and generate human language.
An AI system that can autonomously plan and execute multi-step tasks to achieve a goal.
A technique that combines LLMs with retrieval of external information to ground responses in facts.
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