Ask HN: Where to learn the cutting edge of prompt engineering? I’m doing experiments with LLMs and I’m trying to research techniques for grounding. Example prompt templates, for instance. There’s lots of generic articles about grounding, but details and specific examples are thin on the ground. I’ve read the source for langchain to find the prompt template for agent based reasoning, but that was just one perspective…are there better ways? |
Answer enough questions, stay active enough, and you'll see the same patterns emerge. You'll probably make a lot of mistakes. You'll be corrected by other regulars and people you try to help will send you angry messages saying your prompt didn't work when utilised in the industry. It's a good way to learn. As a little bonus, if you do it constantly enough, OpenAI will give you this little "Regular" rank with a secret forum and such.
Langchain feels a little outdated IMO. I feel like OpenAI's in built tools might be a little ahead of it. It was originally designed to handle memory on the old completion API, but since OpenAI's chat API was released, it's not as useful. There's still good reason to use their completion models though - it performs higher quality responses for some creative uses. Agents built on them don't seem very impressive and OpenAI has their own "assistants" for agent-like stuff: https://platform.openai.com/docs/assistants/how-it-works
That's being too generous lol
Then you will find the answer that works for you, and probably well more thought out than 3/4 of the articles you will find regarding this sort of thing.
I personally stay abreast of new models coming out and run an evals set against new models to assess their performance vs other models (say, gpt-2, gpt-3.5-turbo, etc, gpt-4.)
In terms of grounding, there is RAG, which can be built in any number of ways (PG+pg_vector, vector store, graph db). I would look at arxiv.org publicatons to stay on top of SOTA prompting stuff, as well as adjacent publications (LLMs, scaling, other things)
- https://arxiv.org/
- https://www.microsoft.com/en-us/research/group/dynamics-insights-apps-artificial-intelligence-machine-learning/articles/prompt-engineering-improving-our-ability-to-communicate-with-an-llm/
- https://cloud.google.com/blog/products/ai-machine-learning/how-to-use-grounding-for-your-llms-with-text-embeddings
- https://amatriain.net/blog/hallucinations
and general resources:
- https://learnprompting.org
- https://www.promptingguide.ai
- https://github.com/dair-ai/Prompt-Engineering-GuideIs this obsolete? Does it contain the cutting edge prompt engineering techniques such as saying you'll tip 200$ for a correct answer?
Annoying that is for subs only but If nothing else the graphic representation is good.
I'm reading some papers on arxiv right now, and trying to implement them in our codebase at work. Those papers usually involve doing some common sense thing and measuring the results. Anyone could have come up with it, but they did the data science and showed some evidence it worked.
If there is a better way, I would love to know lol
In practice, some models (ChatGPT in particular) are not deterministic. This makes reproducing things harder. Not impossible, but harder.