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What are the best prompt engineering techniques?

Core techniques that consistently work: (1) Role + objective + constraints to reduce ambiguity; (2) Few-shot examples to anchor style and format; (3) Chain-of-thought or stepwise checklists to tackle reasoning; (4) Self-critique/reflection passes to catch errors; (5) Tool-using prompts (when available) to ground answers in retrieval or calculators; (6) Guardrail snippets that define out-of-scope behavior. The best technique is the one that fits your task and cost budget. For simple classification, a short deterministic prompt is ideal. For complex decisions, staged prompting with verification saves time later.

In practice

Turn reasoning into a checklist the model follows. Then ask it to verify each step against the checklist before finalizing.

Where to start

  1. [01]Pick one business task and implement three techniques; compare results.
  2. [02]Convert your chain-of-thought into a compact checklist for production.
  3. [03]Create a self-critique rubric tailored to your domain.
  4. [04]Document which techniques map best to which task types.
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Related questions

// 001What programming languages are used in prompt engineering?// 002What are the most advanced prompt engineering patterns?// 003Why do checklists improve AI outputs?// 004What is self-critique prompting and when should I use it?

Build it properly.

Promptise Foundry treats prompts as software — typed blocks, versioning, guards and an inspector that shows you every assembly decision.

Prompt engineering in FoundryRead the guides
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