AI & Research
What Is Prompt Engineering?
Designing the input to a language model so the output is reliable enough to build on, including the differences between how the major models expect to be asked.
Definition
Prompt engineering is the practice of structuring the instructions, context and examples given to a language model so it produces the intended output consistently. It is an empirical discipline: prompts are written, tested against real cases, and revised based on where they fail.
Techniques that apply to every model
- Be specific about the task, the audience, the format and the length
- Put durable instructions in the system prompt and the variable material in the user turn
- Give a few worked examples, since demonstrated format beats described format
- Ask for reasoning before the answer when the task involves several steps
- Define the failure case, for example what to output when the answer is not in the source
- Test against a fixed set of inputs so a prompt change can be compared rather than guessed at
Claude
Anthropic's documentation recommends XML style tags to separate the parts of a prompt, for example wrapping source material in document tags and instructions in instructions tags. This makes boundaries unambiguous. Role setting through the system parameter is effective, and for extended thinking models Anthropic advises giving high level direction on how to approach a problem rather than prescribing each step.
GPT models
OpenAI's guidance emphasises clear delimiters between instruction and content, asking the model to adopt a persona, splitting complex tasks into simpler subtasks, and giving the model time to reason before concluding. Structured outputs and function calling are the reliable way to get machine readable results rather than asking for JSON in prose.
Gemini
Google's prompt design guidance frames prompting as test driven and iterative. It recommends system instructions for role, context and formatting rules, few shot examples over zero shot for anything with a specific output shape, and breaking complex requests into ordered steps. Google notes that adding examples is often the single most effective change.
A practical note on portability
Prompts do not transfer cleanly between model families. A prompt tuned with XML tags for Claude may be merely acceptable on another model, and a prompt that depends on one provider's structured output feature will not run elsewhere at all. Keep the task definition and the provider specific scaffolding separate so the first can be reused.
Prompt engineering is a test-driven and iterative process that can enhance model performance.