What's prompt?
Prompt is any interaction between you and LLM (Large Language Model)
- The message you sent to ChatGPT
- Content when you send a request to LLM API
- or the text message you sent to image/music generation models.
The reality is, output from LLM is as good as your prompt. That's why we need to create better prompts to get better results in any field – concise, clean and structured prompts lead to more successful outcomes.
Always clean and concise.
Just like we get better results when we clearly explain a task to an intern, we should think of AI as an intern too. (Yes, AI is a powerful tool, but when we interact with it, we need to explain it like it's 5!)
Examples and references
OpenAI discovered the same thing 5 years ago. ("Language Models are Few-Shot Learners"). There was only GPT-3 back in the time, and its performance not that good. But, prompt with examples was really common. template was Q A Q A. Nowadays probably all AI product has examples in the prompt. Write at least 1 example!
from Twitter, Aravind Putrevu
If task is hard, step by step.
Especially the problems in math or physics needs you to solve it in a step-by-step process. As we can't solve these kind of problems instantly, we can't expect AI to do so either. Plus, AI is an energy-intensive model (due to GPUs and architecture), so expecting it to solve a complex task in one go isn't realistic.
Only adding (Think step by step), improves the results. Also explaining how model should reason, like giving a direction will drive model to solve it in right way.
Role and task info
In specific topics, we need to define the role of the AI! Ex: When you give a task in law, you should say "You're a lawyer, respond based on that."
In this way, model focuses only on that topic in semantic space. This is where using system prompts can be extremely beneficial.
XML XML XML
Add XML tags whenever you add content, or any info, it's like structuring the prompt. For example, we could wrap company information in <company></company> tags or request a json-formatted response by defining a structure like {answer: <your answer>}. When you use XML tags, don't forget to explain it in the prompt, like I'm going to use
prefill
Some models accepts prefill for the answer. You can push the LLM to answer in your way. If you want json format, you can say start with {"answer":, and increase the accuracy + reliability.
divide and conquer an LLM!
For tasks requiring multiple prompts, such as question-generation pipelines, we often get better results by sending several prompts in sequence:
- A prompt that generates the question.
- A prompt that checks if the question is correctly formatted and gives feedback.
- A prompt that refines the question based on that feedback.
Trying to do all of this in a single prompt will cause hallucinations and errors, whereas a chained approach gives more reliable outcomes.
Leaked prompts: link