TLDR;
This video provides a comprehensive guide to prompt engineering for large language models (LLMs). It covers essential concepts, settings like output length and temperature, and various prompting techniques such as zero-shot, few-shot, chain of thought, and tree of thoughts. The video also discusses practical applications like using code and agentic frameworks to improve prompt effectiveness.
- Understanding how LLMs predict outputs based on prompts.
- Adjusting settings like temperature to control creativity.
- Utilizing techniques like chain of thought for complex tasks.
- Employing automatic prompt engineering to refine prompts.
- Staying updated with the latest model capabilities for effective prompting.
Introduction to Prompt Engineering [0:00]
Prompt engineering involves crafting effective inputs for AI models like ChatGPT, Gemini, and Claude to get desired outputs. These models predict outputs based on the input prompt, making the structure and wording of the prompt crucial. Effective strategies depend on the model's capabilities and token limits. LLMs function as prediction engines, taking text input and predicting subsequent tokens (words) based on their training data. Prompt engineering is about designing high-quality prompts that guide LLMs to produce accurate outputs.
Basic Terms: Output Length and Sampling Controls [2:03]
Large language models differ, making it important to understand their settings. Output length determines the maximum number of tokens a model outputs. Reducing output length doesn't make the response more succinct; it simply stops the model when the limit is reached. Sampling controls, including temperature, top K, and top P, influence token selection. Temperature controls randomness; higher temperatures yield more creative responses, while lower temperatures produce more consistent outputs. Top K selects from the top K most likely tokens, and top P limits the vocabulary based on cumulative probability.
Prompting Techniques: Zero-Shot, One-Shot, and Few-Shot [7:56]
Zero-shot prompting involves providing a description of the task without examples. One-shot prompting gives one example, while few-shot prompting provides multiple examples to guide the model. The number of examples needed depends on the task's complexity, the quality of the examples, and the model's capabilities. Few-shot prompting is useful for specifying the desired output format.
System Message, Contextual Prompting, and Role Prompting [11:29]
These techniques involve getting the model to adopt a specific role. System prompting sets the overall context and purpose. Contextual prompting provides specific details relevant to the task. Role prompting assigns a character or identity to the model, influencing its responses. Frameworks like Crew AI use role prompting effectively by defining agents' functions, goals, and backstories.
Step-Back Prompting [14:38]
Step-back prompting involves asking the model a general question related to the task before providing the specific prompt. This activates relevant background knowledge, leading to more accurate and insightful responses. It encourages critical thinking and the application of knowledge in creative ways.
Chain of Thought Prompting [17:24]
Chain of thought prompting involves asking the model to think step by step and show its work. This improves the quality and accuracy of outputs, especially for smaller models without built-in thinking modes. Many recent models do this by default. Combining chain of thought with few-shot prompts can be particularly effective for tasks in STEM fields, logic, and reasoning.
Self-Consistency Prompting [20:49]
Self-consistency combines sampling and majority voting to generate diverse reasoning paths and select the most consistent answer. This involves running the same prompt multiple times and having the model vote on the best solution, improving accuracy and coherence. However, it comes with higher costs and latency.
Tree of Thoughts Prompting [23:50]
Tree of thoughts allows the LLM to explore multiple reasoning paths simultaneously, using a combination of self-consistency and chain of thought. This approach is well-suited for complex tasks requiring exploration but typically needs to be implemented with code or a framework.
ReAct: Reason and Act Prompting [25:13]
ReAct (Reason and Act) prompting enables LLMs to solve complex tasks by combining natural language reasoning with external tools like search and code interpreters. It mimics human operation by using a thought-action loop. Frontier models often have ReAct built-in, but it can also be implemented on smaller models using frameworks like Langchain or CrewAI.
Automatic Prompt Engineering [28:22]
Automatic prompt engineering involves using AI to write prompts. This can be done by providing a basic prompt and asking the model to refine it using techniques like chain of thought or self-consistency. Another approach is to have the model write code to solve a problem, ensuring accuracy.
Best Practices and Conclusion [31:01]
Best practices include providing examples, designing with simplicity, being specific about the output, using instructions over constraints, and controlling the max token length. For high-scale use cases, using variables in prompts is important. Staying up to date on model capabilities is crucial for effective prompting.