Prompt Engineering Guide - From Beginner to Advanced

Introduction to Prompt Engineering 00:00

• Prompt engineering involves strategies to optimize interactions with artificial intelligence models like ChatGPT, Gemini, and Claude for desired outputs.
• Understanding how models process prompts is crucial; they take input text and predict the next word or token based on training data.

Key Concepts in Prompt Engineering 01:54

• Tokens are units of text that models use to understand and generate responses, with prompt structure significantly affecting output quality.
• Output length and sampling controls (temperature, top K, top P) are important settings to tailor responses based on needs.

Prompting Techniques Overview 07:57

• General prompting techniques include zero-shot (no examples), one-shot (one example), and few-shot (multiple examples) prompting to guide the model's output.
• Contextual prompting and role prompting help models adopt specific behaviors or contexts for more relevant responses.

Advanced Prompting Techniques 14:56

• Step back prompting encourages models to consider broader concepts before addressing specific tasks, enhancing the depth of responses.
• Chain of thought prompting allows models to articulate their reasoning process, improving accuracy, especially in logical tasks.

Self-Consistency and Tree of Thoughts 20:50

• Self-consistency involves running the same prompt multiple times to find the most consistent answer among varied outputs.
• Tree of thought prompting facilitates exploring multiple reasoning paths simultaneously, useful for complex tasks.

React Prompting 25:22

• React prompting combines reasoning with external tools to solve complex real-world tasks, simulating human-like problem-solving processes.

Automatic Prompt Engineering 28:38

• This technique involves using AI to automate the creation of detailed prompts based on initial simple descriptions, streamlining the prompting process.

Best Practices for Effective Prompting 31:01

• Always provide examples to guide the model, start with simple prompts, specify expected output formats, and use instructions rather than constraints.
• Stay updated on model capabilities to effectively format prompts and optimize interactions with AI systems.