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I understand what you are saying. When OpenAI's API was still new, there was a LOT of "prompt engineering" required - a lot of proper formatting and tricks to g
by jasongill 3y ago
I understand what you are saying. When OpenAI's API was still new, there was a LOT of "prompt engineering" required - a lot of proper formatting and tricks to get it to produce the required output, proper ways of formatting prompts with "few-shot" examples, etc. Packages like LangChain sprang up because trying to chain together multiple prompts with different inputs and outputs was becoming a bear.
But then, within a few months, the LLM's got better and better to the point where the average ChatGPT user doesn't need to be a "prompt engineer" to get what they want. Sure, there are tricks with adding specific phrases to a prompt to get it to do a better job, but what we now consider "prompt engineering" and what was "prompt engineering" pre-ChatGPT are very different.
LangChain, for example, has reacted by pivoting away from just prompt chaining and templating and into an ecosystem for agents and automated AI "bots". I think that may be overkill for many use cases, and there are good alternatives (Rivet is now my go-to) if you still want to build an LLM-powered app that just requires a specific logical path and specific inputs and outputs, but honestly if I was working at an "AI startup" I would probably be trying to focus my time on getting away from "prompts" and more just to "doing a good job of what you are told".
I do think that "prompts" (especially chat-style conversations) lack a lot of the nuance that you need for a production-grade app especially when it comes to specific input and output formats, etc. Function calling helps a lot but I don't see why xAI is focusing on an IDE for writing a great prompt instead of building an LLM that can make good, structured output with just an "ok" prompt.
The next year will be interesting - it hasn't even been a full year since ChatGPT was released, and look how far the industry has come since the closed-beta release of GPT-3.
- heisgone 3y agoFor me, I would just like a simple way (no technical knowledge) to invoke LLM sub-steps. Exemple "Find the 4 articles of laws most relevant to this case, then anaylse each of those articles separatly". So, a simple non-technical way of doing auto-gpt style substeps. This would greatly increate ChatGPT precision.