prompt-engineering-patterns

Developer Tools

Gives your agent the ability to master advanced prompt engineering techniques for maximum LLM performance and reliability.

When to use

When designing complex prompts for production AI applications
When you need to implement structured reasoning like chain-of-thought
When building few-shot learning systems with dynamic example selection

How to use

1
Define the structured output schema using Pydantic for reliable parsing
2
Implement chain-of-thought prompting to elicit step-by-step reasoning
3
Select and balance few-shot examples to handle complex edge cases
4
Optimize prompt performance through iterative A/B testing and refinement

Example conversation

case

You

Help me build a prompt that extracts sentiment and key phrases into JSON.

Agent

I will design a structured prompt using a Pydantic schema to ensure the model returns valid JSON with 'sentiment', 'confidence', and 'key_phrases' fields. I'll include few-shot examples to guide the extraction and add a reasoning step to improve accuracy. The result will be a type-safe prompt template ready for production use.

FAQ

Related Skills

ai-prompt-engineering-safety-reviewDeveloper Tools

Gives your agent the ability to conduct comprehensive safety, bias, and security reviews of AI prompts while providing optimized improvements based on industry best practices.

e2e-testing-patternsDeveloper Tools

Gives your agent the ability to build, debug, and maintain reliable end-to-end test suites using Playwright and Cypress best practices.

error-handling-patternsDeveloper Tools

Gives your agent the ability to implement robust error handling patterns across multiple languages to build resilient and fault-tolerant applications.

ui-component-patternsDeveloper Tools

Gives your agent the ability to build reusable React components following modern patterns like composition and compound components.

python-testing-patternsDeveloper Tools

Gives your agent the ability to implement comprehensive Python testing strategies using pytest, fixtures, and mocking.

prompt-engineering-patterns

Developer Tools

Gives your agent the ability to master advanced prompt engineering techniques for maximum LLM performance and reliability.

When to use

When designing complex prompts for production AI applications
When you need to implement structured reasoning like chain-of-thought
When building few-shot learning systems with dynamic example selection

How to use

1
Define the structured output schema using Pydantic for reliable parsing
2
Implement chain-of-thought prompting to elicit step-by-step reasoning
3
Select and balance few-shot examples to handle complex edge cases
4
Optimize prompt performance through iterative A/B testing and refinement

Example conversation

case

You

Help me build a prompt that extracts sentiment and key phrases into JSON.

Agent

FAQ

Related Skills

ai-prompt-engineering-safety-reviewDeveloper Tools

Gives your agent the ability to conduct comprehensive safety, bias, and security reviews of AI prompts while providing optimized improvements based on industry best practices.

e2e-testing-patternsDeveloper Tools

Gives your agent the ability to build, debug, and maintain reliable end-to-end test suites using Playwright and Cypress best practices.

error-handling-patternsDeveloper Tools

Gives your agent the ability to implement robust error handling patterns across multiple languages to build resilient and fault-tolerant applications.

ui-component-patternsDeveloper Tools

Gives your agent the ability to build reusable React components following modern patterns like composition and compound components.

python-testing-patternsDeveloper Tools

Gives your agent the ability to implement comprehensive Python testing strategies using pytest, fixtures, and mocking.

Product

Community

prompt-engineering-patterns

When to use

How to use

Example conversation

FAQ

Related Skills

Product

Community

prompt-engineering-patterns

When to use

How to use

Example conversation

FAQ

Related Skills

Product

Community