Prompt Engineering for Developers: Beyond Basic Chat
Practical prompt engineering techniques for building AI features — structured outputs, chain-of-thought, few-shot learning, and evaluation strategies.
Prompts Are Code
Treat your prompts like code: version them, test them, and review them. A poorly written prompt will give inconsistent results no matter how powerful the model.
Technique 1: Structured Output
Always request structured output for programmatic use.
Technique 2: Chain of Thought
For complex reasoning, make the model show its work.
Tip
Chain-of-thought prompting dramatically improves accuracy for reasoning tasks, even if you discard the reasoning from the final output.
Technique 3: Few-Shot Learning
Show examples of desired input/output pairs.
Evaluation
Build an eval suite for your prompts. Run evals on every prompt change, just like unit tests for code.
Written by
Elena Vasquezauthor
AI/ML engineer exploring the intersection of machine learning and web development. Contributor to TensorFlow.js and Hugging Face.
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