pal
Prompt-Engineering-Guide
pal | Prompt-Engineering-Guide | |
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4 | 82 | |
436 | 43,924 | |
1.4% | 2.3% | |
3.1 | 9.7 | |
10 months ago | 6 days ago | |
Python | MDX | |
Apache License 2.0 | MIT License |
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pal
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Prompt Engineering Guide: Guides, papers, and resources for prompt engineering
Using the terminology that I'm working with this is an example of a second-order analytic augmentation!
Here's another approach of second-order analytic augmentation, PAL: https://reasonwithpal.com
And third-order, Toolformer: https://arxiv.org/abs/2302.04761
The difference isn't in what is going on but rather with framing the approach within the analytic-synthetic distinction developed by Kant and the analytic philosophers who were influenced by his work. There's a dash of functional programming thrown in for good measure!
I have scribbled on a print-out of the article on my desk:
Nth Order
- [R] Faithful Chain-of-Thought Reasoning
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GPT-3: Techniques to improve reliability
GitHub: https://github.com/reasoning-machines/pal
tl;dr -- LLMs are bad at basic arithmetic and logic (as their opening examples with math word problems show), but they do much better if instead of asking them for the answer, you ask for code to compute the answer. Then evaluate or run the code to get the answer.
Prompt-Engineering-Guide
- FLaNK AI - 15 April 2024
- Prompt Engineering Guide
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24 GitHub repos with 372M views that you can't miss out as a software engineer
Guides, papers, lecture, notebooks and resources for prompt engineering: https://github.com/dair-ai/Prompt-Engineering-Guide
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Resources to deepen LLMs understanding for software engineers
this has been a great resource. approachable and great for practitioners. it's frequently updated with new papers and techniques https://www.promptingguide.ai/
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Step-by-Step Guide to building an Anomaly Detector using a LLM
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.
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The Essential Guide to Prompt Engineering for Creators and Innovators
Prompt Engineering Guide
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Getting Started with Prompt Engineering
Let's try to understand what is Prompt Engineering is all about. Here's the quote from Prompt Engineering Guide. DAIR-AI
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Microsoft/promptbase: All things prompt engineering
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/
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OpenAI: Prompt Engineering
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)
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Ask HN: Where to learn the cutting edge of prompt engineering?
- https://www.promptingguide.ai