Advanced Prompt Engineering Patterns

Chain of Thought

Once you're comfortable with basic prompting, these patterns unlock significantly better results for complex tasks.

Asking the model to "think step by step" isn't just a trick — it fundamentally changes how the model allocates compute to reasoning. For math, logic, and multi-step code generation, this is essential.

Few-Shot Examples

Providing 2-3 examples of input→output pairs teaches the model your expected format more reliably than verbal description alone. The examples serve as implicit constraints.

Choose examples that cover edge cases. If you only show happy-path examples, the model will struggle with error cases. Include at least one example that demonstrates how to handle unexpected input.

Choosing Good Examples

The quality of your few-shot examples matters more than quantity. Three well-chosen examples outperform ten mediocre ones. Each example should illustrate a different aspect of the task.

Self-Consistency

Generate multiple responses and take the majority answer. This works surprisingly well for factual questions and code review tasks where the model might occasionally hallucinate.

The cost of generating 3-5 responses is often worth it for high-stakes tasks like security reviews or data migration scripts.

Meta-Prompting

Use the AI to help write better prompts. Describe what you want to achieve, then ask the model to generate an optimal prompt for that task. This recursive approach often produces prompts you wouldn't have thought of yourself.

Prompt Chaining

Break complex workflows into a chain of focused prompts, where each step's output feeds into the next. This is more reliable than asking for everything in one go, and easier to debug when something goes wrong.

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