prompt-engineering-patterns

Structured JSON outputs from LLMs are achieved by applying schema constraints and JSON-friendly response formatting. This approach ensures reliable parsing and reduces malformed outputs in production systems.

Few-shot learning prompts are designed by selecting representative examples and structuring them to handle edge cases effectively. This pattern improves LLM accuracy and consistency for complex tasks.

Chain-of-thought prompt engineering improves reasoning quality by guiding the LLM through step-by-step logic and verification workflows. This mechanism catches mistakes early and enhances overall output reliability.

Prompt templates can be integrated with RAG pipelines by applying specific integration patterns. This pairs parameterized prompts with validation layers to maintain controllability in production systems.

LLM output formats break during multi-turn conversations due to inconsistent context handling and lack of structured templates. Applying reusable, parameterized prompt systems resolves these formatting inconsistencies.

Prompt templates are optimized for token usage and latency through parameterized iteration and structured design. This process yields measurable performance improvements while maintaining output accuracy.