YOyoungbowie·Feb 7, 2024
In Ask HN: Any good collection of writing prompts for GPT 3.5/4?brexhq
prompt-engineering
AI & Machine Learning2023–2024
brexhq / prompt-engineering
prompt-engineering
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
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Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
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What Hacker News says
10 sourceshttps://github.com/kevinamiri/Instructgpt-prompts
https://github.com/tg12/gpt_jailbreak_status
https://www.reddit.com/r/ChatGPT/comments/13cklzh/what_are_s...
https://github.com/brexhq/prompt-engineering
https://github.com/dave1010/tree-of-thought-prompting
In Ask HN: Any good collection of writing prompts for GPT 3.5/4?An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).
You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react
In LLM Agent Paper ListI recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.
These are good straight-to-the-point guides:
- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering
- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...
- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...
- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...
- (~hr) Karpathy talk: https://www.youtube.com/watch?v=bZQun8Y4L2A
tl;dr:
- Begin with the best and most forgiving mo
Read the rest
del available, optimizing it later if necessary.
- Write your prompts in a clear, specific, and detailed manner
- Be mindful, however, of the tradeoff between including more detail and increasing latency or cost.
- If you're doing any complex reasoning, ask the model to show its work, help "stretch out" computation over more tokens
- "Escape" any included or quoted text properly to avoid confusing the model.
- Keep track of your token budget, considering the context window in conversations and response length.
- Employ an iterative process of measuring, adjusting, and improving your prompt engineering techniques.
Author of the guide here. I attempt to address this in the "Why do we need prompt engineering?"[^1] section.
> ... we used an analogy of prompts as the “source code” that a language model “interprets”. Prompt engineering is the art of writing prompts to get the language model to do what we want it to do – just like software engineering is the art of writing source code to get computers to do what we want them to do.
Borrowing from Oxford Dictionary, the definition of "engineering" is:
> the branch of science and technology concerned with the design, building, and use of engines, machines, and structures.
I think it's pretty reasonable to say that "prompt engineering" falls squarely in the realm of "technology concerned with the use of a machine".
[^1]: https://github.com/brexhq/prompt-engineering#why-do-we-need-...
In Brex’s Prompt Engineering GuideThanks for pointing this out. That was my mistake – my brain must have swapped out "different transformer architectures" with "different model architectures".
I just updated the guide: https://github.com/brexhq/prompt-engineering/commit/3a3ac17a...
In Brex’s Prompt Engineering GuideBrex’s Prompt Engineering Guide
540 points · Open discussionBrex's Prompt Engineering Guide
3 points · Open discussionDirect link to the guide: https://github.com/brexhq/prompt-engineering
In Brex’s GPT-4 Prompt Engineering GuideBrex Open-Sourced Their Prompt Engineering Crash Course
2 points · Open discussion