Short answer
Prompt engineering is the practice of designing instructions, examples, and context so models reliably perform a task within product constraints.
Why it matters
Prompts are product code. Versioning, evaluation, and injection defenses turn demos into maintainable systems.
Article
Method
Keep an eval set; change one prompt variable at a time.
Examples
Structured extraction
Instead of “extract the fields,” a prompt provides a JSON schema, two examples, and instructions to return nulls when absent—reducing invented values.
Coding change request
A developer specifies file paths, failing test output, coding standards, and “do not change public APIs,” yielding a tighter patch from an assistant.
FAQ
Is prompt engineering still relevant with better models?
Yes. Clearer tasks, better context selection, and output contracts remain valuable even as base models improve. The emphasis shifts from tricks toward systematic templates and evals.
What is a system prompt?
A system-level instruction (supported by many APIs/apps) that sets enduring behavior—role, style, safety boundaries—separate from the user’s latest message.
How do I know a prompt change helped?
Hold out a test suite of representative tasks, score outputs with rubrics or automated checks, and compare versions. Anecdotes alone mislead.
Sources
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OpenAI prompting guidance
OpenAIOfficial prompting concepts for OpenAI models.
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Anthropic documentation
AnthropicOfficial documentation including prompt and tool-use guidance for Claude.
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Google AI prompting strategies
GoogleOfficial prompting strategies for Gemini models.