Prompt-Engineering-Guide VS openai-python

Prompt Engineering Guide is the holy grail of all guides, aiming to make it easier to stay up-to-date with prompt engineering guides, techniques, applications, and papers. If you are getting started, this is an excellent place to start.

Guides, papers, lecture, notebooks and resources for prompt engineering: https://github.com/dair-ai/Prompt-Engineering-Guide

this has been a great resource. approachable and great for practitioners. it's frequently updated with new papers and techniques https://www.promptingguide.ai/

The idea behind prompt engineering is to construct the queries given to the language models to optimise their performance. This helps to guide them to generate the desired output by fine-tuning their response. There is a plethora of research papers out there on different forms of prompt engineering. DAIR.AI published a guide on prompt engineering that you might find useful to get started.

Prompt Engineering Guide

Let's try to understand what is Prompt Engineering is all about. Here's the quote from Prompt Engineering Guide. DAIR-AI

I found this resource [0] handy for getting a grasp on all the different terms people use (zero/one-shot, tree of thoughts, RAG, etc). It's not super detailed, but was enough for me (a professional developer) to get started on some side projects with Mistral.

[0] https://www.promptingguide.ai/

There are better guides out there too

- https://www.promptingguide.ai/readings

- https://github.com/dair-ai/Prompt-Engineering-Guide/tree/mai...

- https://github.com/microsoft/promptbase (this one is less of a guide, but is likely the current SoTA)

The ongoing model anchoring/grounding issue likely affects all GPT-4 checkpoints/variants, but is most prominent with the latest "gpt-4-turbo-2024-04-09" variant due to its most recent cutoff date, might imply deeper issues with the current model architecture, or at least how it's been updated:

https://github.com/openai/openai-python/issues/1310

See also the original thread on OpenAI's developer forums (linked on the GitHub issue) with confirmations from others.

A test code snippet is included in the GitHub issue to A/B test the problem yourself with your own questions if need be.

- Iterate on large codebases quickly: we use Rust for maximum performance

GritQL has already been used on thousands of repositories for complex migrations[1] but we're excited to collaborate more with the open source community.

[1] Ex. https://github.com/openai/openai-python/discussions/742

Explore OpenAI and learn how to easily integrate it into your existing application with the OpenAI Python module.

You tried to access openai.Completion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742

Relatedly, I checked and OpenAI deleted all references to their ChatML spec from their GitHub repo.

This is what it said in an earlier commit: https://github.com/openai/openai-python/blob/2942bf4bb635b1e...

Make sure to install the OpenAI library first. Since I'm using Python, I need

Source: https://github.com/openai/openai-python/discussions/742