prompt-engineering-patterns

Optimize prompts by applying structured prompt engineering patterns, such as modular templates, few-shot example selection, and verification steps, to produce consistent LLM outputs and reduce hallucinations. This converts ad-hoc instructions into maintainable, reliable workflows.

The best way to design few-shot examples is using semantic and diverse example selection strategies, difficulty scaffolding, and error-guided inclusion. These prompt engineering patterns improve LLM generalization and prevent fragile few-shot examples from causing inconsistent outputs.

Elicit chain-of-thought reasoning by implementing specific prompt patterns for step-by-step reasoning, self-consistency, and verification flows. These prompt engineering techniques guide LLMs through complex reasoning, math, and debugging tasks to ensure accurate, verifiable outputs.

Yes, you can use prompt templates with retrieval-augmented generation pipelines. Specific prompt engineering integration patterns combine modular templates with RAG, validation pipelines, and caching to reduce latency and enable metrics-driven optimization.

Reduce token cost in production LLM prompts by applying token-efficiency techniques and modular, conditional template designs. These prompt optimization strategies streamline system prompts for chat and single-turn APIs while maintaining high-quality outputs.

Your LLM prompt produces inconsistent answers due to unstructured system prompts and fragile few-shot examples. Applying iterative A/B testing workflows, prompt templates, and verification steps resolves these failure modes and stabilizes production prompt performance.