prompt-engineering-patterns

Prompt engineering patterns are reusable, production-tested structures for designing system prompts. You need them when building generative AI features to prevent failure modes such as vague outputs, reasoning errors, or format inconsistency.

To get consistent structured output from an LLM, apply the Structured Output pattern to your system prompt. This prompt engineering technique forces the model to generate responses in a parsable JSON format, mitigating format inconsistency issues in AI pipelines.

The best way to design system prompts for a customer support chatbot is using the Multi-Shot Conversation pattern. This approach ensures the bot maintains a natural, helpful dialogue flow while mitigating reasoning errors through production-tested prompt design.

To apply the Role + Constraint pattern, use a prompt engineering library to structure your system prompt with specific persona definitions and strict behavioral rules. This technique constrains the generative AI model's outputs to predictable, high-quality responses.

Vague outputs and reasoning errors occur when prompts lack structured guidance. You can fix these failure modes by applying prompt engineering patterns like Chain-of-Thought, which structures the model's reasoning process to ensure reliable, high-quality AI outputs.

Yes, these production-tested prompt engineering patterns apply across generative AI pipelines. The library provides 9 reusable patterns for selecting prompting techniques and reviewing prompts, ensuring reliable AI output regardless of your specific LLM implementation.