Prompt Engineering Fundamentals
Learn Anthropic's documented prompt engineering techniques — clear instructions, multishot examples, chain-of-thought, and XML structuring — by writing and testing prompts directly against the Claude API.
Prerequisites: Python 3.9+, an Anthropic API key from console.anthropic.com, and basic familiarity with running Python scripts.
Prompt engineering is the fastest, cheapest way to improve an LLM's output — faster and cheaper than fine-tuning, and it works across model updates without retraining. In this project you'll write a small Python test harness, then apply four of Anthropic's core documented techniques to the same task (categorizing customer feedback) and observe the difference each one makes: being clear and direct, using multishot examples, encouraging step-by-step reasoning, and using XML tags to structure input and output.
Everything here follows Anthropic's official prompt engineering documentation rather than generic prompting folklore — Anthropic's docs are explicit that tricks aimed at bypassing model behavior tend to stop working as models evolve, so the techniques below are the straightforward, durable ones.
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