Prompt Engineering

Prompt Engineering is the discipline of structuring inputs (prompts) to Large Language Models (LLMs) to reliably and efficiently elicit a desired, high-quality output.

This is the core skill for maximizing performance from models like GPT-4 and Claude 3: it's the art and science of guiding an AI. The process involves systematic iteration and applying specific techniques to control the model's behavior and reduce 'hallucination.' Key advanced methods include Chain-of-Thought (CoT) prompting, which forces the LLM to process complex problems step-by-step, and Few-Shot prompting (providing 2-3 examples) to establish a clear output format or style. Mastery of these methods directly translates to tangible gains: improved accuracy, reduced API costs from fewer retries, and production-ready outputs for applications like customer service bots or code generation.

https://www.promptingguide.ai/