prompt-engineering-patterns

Achieve reliable LLM outputs by applying structured prompt engineering patterns such as few-shot learning, chain-of-thought reasoning, and JSON schema enforcement to constrain and validate model behavior.

The best way to enforce structured JSON outputs is by using prompt patterns designed for schema enforcement and robust parsing, ensuring the LLM generates data that strictly matches your expected format.

Design safe assistant system prompts by applying template patterns that define role behavior, establish safety constraints, and maintain consistent multi-turn personalities.

Chain-of-thought prompting improves reliability by enforcing step-by-step reasoning, enabling self-consistency checks and verification steps that reduce logical errors in complex tasks.

Yes, you can use prompt template systems that support variable interpolation and conditional sections to build reusable components, allowing dynamic context-aware demonstrations for few-shot learning.

Few-shot learning limitations include context window constraints and example selection bias, requiring iterative refinement and A/B testing performance metrics to maintain output quality across domains.