prompt-engineering-patterns

Prompt engineering patterns solve poor output quality by structuring prompts with clear instructions, system roles, and reasoning frameworks. Chain-of-thought prompting, few-shot examples, and template systems enable reliable, consistent LLM performance in production applications.

Design production prompts by combining system prompt guidelines, few-shot learning with dynamic example selection, and chain-of-thought reasoning. Template systems with variable interpolation and conditional blocks create modular, versioned prompts that scale reliably across deployment scenarios.

Few-shot learning adds examples to prompts to guide LLM behavior. Example selection strategies use semantic similarity and diversity to retrieve relevant demonstrations dynamically, reducing token waste while improving reasoning accuracy and task-specific performance.

Yes. Prompt templates integrate seamlessly with RAG systems through modular prompt components and versioning. System prompts, few-shot examples, and chain-of-thought reasoning combine with retrieved context to produce reliable outputs across retrieval-augmented workflows.

Prompt optimization requires iterative refinement and A/B testing to measure performance. Token efficiency matters for production costs; template systems reduce waste. Robustness depends on consistent system prompt behavior, controlled reasoning steps, and handling edge cases across variable inputs.