prompt-engineering-patterns

Chain-of-thought prompting elicits step-by-step reasoning by instructing LLMs to break down logic incrementally. Techniques like Zero-shot CoT and Self-Consistency guide models through intermediate steps before reaching conclusions, significantly enhancing performance on complex logic and math problems.

Few-shot learning involves selecting semantically similar examples and structuring them to guide LLM behavior. Well-constructed examples demonstrate desired formats and reasoning patterns, improving accuracy and adherence to specified outputs across sentiment analysis, classification, and structured tasks.

Production prompt design requires modular, reusable templates with clear role definitions, structured instructions, and error handling. Versioning and token-efficient design ensure reliability and controllability across deployment contexts while maintaining consistency in LLM responses.

Prompt optimization involves iterative refinement and A/B testing methodologies to compare template variants. This approach identifies high-performing prompt structures, reduces token waste, and maximizes LLM output quality for specific use cases and evaluation metrics.

RAG integration combines retrieval systems with dynamic example selection to populate few-shot prompts with contextually relevant instances. This workflow enhances LLM accuracy by automatically selecting the most appropriate examples based on input characteristics and task requirements.

Prompt debugging identifies failure modes and inconsistencies in LLM outputs. Evaluation hooks and systematic testing catch edge cases early, ensuring prompts perform reliably across diverse inputs before production deployment, reducing runtime failures and output variability.