Prompt Engineering

Prompt Engineering is the craft of designing, structuring, and iteratively refining input text—including system messages, role instructions, and examples—to steer a large language model (LLM) toward accurate, safe, and cost-efficient outputs. Techniques span zero-shot directives, few-shot exemplars, chain-of-thought cues, function-calling schemas, and Retrieval-Augmented Generation (RAG) context injection. Engineers tweak wording, temperature, and stop tokens, then A/B test responses for relevance, latency, and token usage. Automation layers—prompt templates, parameter tuning, and evaluation harnesses—enable CI/CD pipelines that version prompts alongside code. Guardrails add content filters and constitutional clauses to minimize bias or policy violations. By treating the prompt as an API contract between human and model, Prompt Engineering turns a general-purpose LLM into a domain-specific copilot for tasks like drafting legal briefs, writing code, or summarizing research papers.