Prompt Engineering

Effective prompt engineering applies structured techniques to control LLM behavior. The process requires understanding tokenization, context windows, temperature settings, and model-specific response tendencies. Advanced methods include retrieval-augmented generation (RAG) for grounding outputs in source data, tool-use patterns for agent workflows, and evaluation frameworks that measure output quality against defined criteria. Results improve through systematic A/B testing, prompt versioning, and feedback loops tied to production metrics.