Prompt Engineering
Prompt engineering designs the instructions and context given to large language models to elicit accurate, useful responses. It covers prompting strategies like few-shot examples, chain-of-thought reasoning, system prompts, and the iterative testing that improves model output quality for specific tasks.
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Intro
Prompt Engineering
Prompt engineering is the discipline of writing instructions that get a language model to produce the output you need. You are not programming the model. You are communicating with it — giving it context, constraints, and examples so it understands your intent and responds usefully.
The skill matters because the same model can produce wildly different results depending on how you ask. A vague instruction gets a vague answer. A precise instruction with the right structure gets a precise answer. The gap between those two outcomes is what prompt engineering closes.
What prompt engineering is
A prompt is the text you send to a language model. Prompt engineering is the process of designing, testing, and refining that text so the model consistently meets your requirements. It sits between your intent and the model output.
The work is empirical. You write a prompt, test it against examples, measure whether the output meets your criteria, and revise. There is no single correct prompt for a task — only prompts that meet your success criteria more or less reliably. Treat it like writing a specification: the clearer and more complete the spec, the closer the output matches what you need.
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Sources
- https://platform.openai.com/docs/guides/prompt-engineering
Supports
- Prompt engineering is the process of writing effective instructions for a model so it consistently generates content meeting requirements
- Message roles (developer, user, assistant) with differing authority levels
- Developer messages provide system rules, user messages provide inputs (function and arguments analogy)
- Few-shot learning with diverse examples steers model toward new tasks
- Markdown and XML formatting to mark logical boundaries and communicate hierarchy
- Prompt structure sections (identity, instructions, examples, context) in recommended order
- Including relevant context (RAG) to give models access to data outside training
- Context window limits defined in tokens per model
- GPT models benefit from precise explicit instructions
- Reasoning models work better with high-level guidance and less micro-management
- Pinning production apps to specific model snapshots for consistent behavior
- Building tests and evaluation suites to measure prompt behavior
- https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
Supports
- Prompt engineering requires clear success criteria and empirical testing before starting
- Not every failing eval is best solved by prompt engineering (latency and cost may need model selection instead)
- Techniques covered include clarity, examples, XML structuring, role prompting, thinking tags, and prompt chaining
- Interactive tutorial with nine chapters and exercises for hands-on practice
- https://ai.google.dev/gemini-api/docs/prompting-strategies
Supports
- Prompt design is creating natural language requests that elicit accurate high-quality responses from a language model
- Clear instructions, examples, and output constraints as core strategies
- Iterative testing and refinement as standard practice
- https://cookbook.openai.com/examples/gpt4-1_prompting_guide
Supports
- Prompting practices evolve between model versions and require prompt migration
- Context examples, specific clear instructions, and inducing planning via prompting maximize model intelligence
- AI engineering is empirical and nondeterministic requiring informative evals and frequent iteration