Unlock the secrets of effective prompt engineering with our comprehensive guide. Discover how to communicate directly, adopt personas, and fine-tune models to enhance AI interaction. Dive in and learn how to get the most out of LLMs!
Introduction
This blog post will explore essential guidelines and recommendations for crafting effective prompts when interacting with LLM models. We'll cover the importance of simplicity, directness, and specificity, along with advanced techniques like persona adoption and fine-tuning. Ready to optimize your AI communication? Let's get started!
Getting Started: The Basics of Prompt Engineering
Start Simple and Direct
When you first begin interacting with AI models like ChatGPT, simplicity is key. YOU MUST use straightforward language. For instance, instead of saying, "Please tell me about the weather today," you can say, "What's the weather today?" This direct approach helps the model understand your request without unnecessary politeness.
Be Specific and Clear
Specificity is crucial. Vague prompts can lead to ambiguous or irrelevant responses. Instead of asking, "Tell me about history," try, "Tell me about the history of the Roman Empire." This directs the model to provide the exact information you need.
Adopt a Persona
Enhance your interaction by asking the model to adopt a persona. For example, "Respond as a tech expert" or "Answer like a fitness coach." This adds context to the conversation, making the responses more tailored and relevant.
Advanced Techniques for Effective Prompting
Formatting your input will help to better understand what you want and limit the model to answer that question in the expected format.
Use Delimiters for Clarity
Separating different parts of your prompt with delimiters like ### or """ can significantly improve the model's understanding. It does not matter what delimiter you use but be consistent in all the prompt. For instance:
Summarize the text below as a bullet point list of the most important points. Text: """{text input here}"""
Specify Output Format
Instead of asking for a response in a certain number of words, specify the format. For instance, "Provide a 5-paragraph explanation" or "List 10 bullet points."
Also, if you would like to get the output with certain format, for example JSON provide an example of the structure it should have.
Fine-Tuning for Precision
If you're encountering issues with the model's behavior, consider fine-tuning. Fine-tuning involves adjusting the model using a collection of input-output examples to enhance its performance in specific tasks. Check out The process of fine-tuning LLama2 using Python and a single GPU for an example.
Utilizing Multiple Behaviors and Step-by-Step Instructions
Employ Agents and Tools
For tasks requiring diverse behaviors according to the input, use agents with tools. For example, if a user asks for a YouTube video, use an agent to fetch the video instead of trying to handle it all the functionalities in one prompt. Check How to Create an AI Agent Using LangChain and GPT for a detail guidance.
Break Tasks into Steps in the Prompt
When a task requires multiple steps, clearly outline them. Encourage intermediate reasoning steps with prompts like, "Let's think step by step." For instance:
Step 1: Summarize the text in one sentence with a prefix that says "Summary: ".Step 2: Translate the summary into Spanish with a prefix that says "Translation: ".
Chain-of-Thoughts (CoT)
When adding "Let's think step by step." , the model is able to break down the problem into steps, and explaining the reasoning process, where each step logically follows from the previous one.
For example:
I went to the market and bought 20 apples. I gave 3 apples to the neighbor and 5 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with? Let´s think step by step.
Sure, let's break it down step by step:
- Starting apples: You bought 20 apples.
- Gave 3 apples to the neighbor: Subtract 3 from 20, leaving you with 17 apples.
- Gave 5 apples to the repairman: Subtract another 5 from 17, leaving you with 12 apples.
- Bought 5 more apples: Add 5 to 12, giving you 17 apples again.
- Ate 1 apple: Subtract 1 from 17.
So, after all these transactions, you're left with 16 apples.
When adding examples in the prompt, It is proven that if we add also an explanation for that output, the LLM will provide better results. This is highly recommended to consider when performing difficult tasks.
Prompt Chaining
Break complex tasks into subtasks, using each response as input for the next prompt.
Engaging the Model with Emotional and Detailed Prompts
Emotional Stimuli
Adding an emotional aspect can enhance the model's response. For example, "This is very important for my career" can lead to more thoughtful and precise answers.
Encourage Detailed Answers
To get detailed responses, prompt the model to explain like you're an 11-year-old. For example, "Explain to me like I'm 11 years old: How does photosynthesis work?"
BIAS Considerations
Ensure your prompts encourage unbiased responses. For instance, "Ensure that your answer is unbiased and avoids relying on stereotypes."
Adjust LLM Parameters
- Temperature: Lower temperatures make responses more deterministic; higher temperatures introduce randomness.
- Top P: Controls how deterministic the model is by considering the top percentage of tokens.
- Max Length: Sets the maximum number of tokens generated, preventing excessive length and costs.
- Stop Sequences: Specifies a word to stop generation, ensuring concise lists or responses.
- Frequency Penalty: Penalizes repeated tokens, encouraging varied vocabulary.
- Presence Penalty: Penalizes token occurrence uniformly, promoting diversity in responses.
Techniques to Master Prompt Engineering
Zero-Shot
Include one example in the prompt.
Few-Shot
Include multiple examples with the same format. For example:
This is awesome! → Positive
This is bad! → Negative
Wow that movie was rad! → Positive
What a horrible show! →
Explore Prompt Repositories
Before crafting your own prompts, check if others have already tackled a similar problem. Reviewing their successes and challenges can save you time and provide valuable insights. Several hubs offer a wealth of prompt examples across various tasks:
Tools for Automatic Prompt Engineering
Streamline your prompt engineering process with these advanced tools designed to automate and enhance prompt creation. Based on cutting-edge research, these tools provide diverse perspectives and innovative ideas to improve your prompts:
- https://github.com/keirp/automatic_prompt_engineer
- https://github.com/lim-hyo-jeong/Prompt-Enhancer
- https://magicprompts.lyzr.ai/
- https://github.com/keirp/automatic_prompt_engineer
Conclusion
The art of prompt engineering involves a mix of simplicity, specificity, and advanced techniques. By following these guidelines, you can enhance your interactions with LLMs, ensuring they provide accurate, relevant, and valuable responses. Whether you're a beginner or an experienced user, these strategies will help you unlock the full potential of your AI systems.
References
- Prompt Engineering Guide
- 26 principles - https://github.com/VILA-Lab/ATLAS/blob/main/data/README.md
- Best practices for OpenAI, Google, Anthropic, Cohere
- Papers
- Paper LLMs are Optimizers - DeepMind - LLMs iterate to improve solutions
- Paper PromptBreeder . DeepMind - self-improvement via prompt evolution
- Paper LLMs are Human-Level Prompt Engineers - Automatic Prompt Engineering (APE)
- Paper ¨Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4¨
