Prompt-Engineering-Guide
sharegpt
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Prompt-Engineering-Guide
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Top Open Source Prompt Engineering Guides & Toolsđ§đď¸đ
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.
- 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)
sharegpt
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How Open is Generative AI? Part 2
Vicuna is another instruction-focused LLM rooted in LLaMA, developed by researchers from UC Berkeley, Carnegie Mellon University, Stanford, and UC San Diego. They adapted Alpacaâs training code and incorporated 70,000 examples from ShareGPT, a platform for sharing ChatGPT interactions.
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create the best coder open-source in the world?
We can say that a 13B model per language is reasonable. Then it means we need to create a democratic way for teaching coding by examples and solutions and algorithms, that we create, curate and use open-source. Much like sharegpt.com but for coding tasks, solutions ways of thinking. We should be wary of 'enforcing' principles rather showing different approaches, as all approaches can have advantages and disadvantages.
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Thank you ChatGPT
You can see the url in the comment, https://sharegpt.com and if you go there it gives you the option for installing the chrome extension, after that it shouldnât be hard to use it
- The conversation started as what would AI do if it became self aware and humans tried to shut it down. The we got into interdimensional beings. Most profound GPT conversation I have had.
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Ăbersicht aller nĂźtzlichen Links fĂźr ChatGPT Prompt Engineering
ShareGPT - Share your prompts and your entire conversations
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(Reverse psychology FTW) Congratulations, you've played yourself.
Or used https://sharegpt.com
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"Prompt engineering" is easy as shit and anybody who tells you otherwise is a fucking clown.
you can gets lots of ideas here > https://sharegpt.com/ (180,000+ prompts)
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I built a ChatGPT Mac app in just 20 minutes with no coding experience - thanks ChatGPT!
I would love to read the whole conversation: Check out this cool little GPT sharing extension: https://sharegpt.com - that way the code snippets can be copied easily
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Teaching ChatGPT to Speak My Sonâs Invented Language
> Cool, thatâs really the only point Iâm making.
To be clear, I'm saying that I don't know if they are, not that we know that it's not the same.
It's not at all clear that humans do much more than "that basic token sequence prediction" for our reasoning itself. There are glaringly obvious auxiliary differences, such as memory, but we just don't know how human reasoning works, so writing off a predictive mechanism like this is just as unjustified as assuming it's the same. It's highly likely there are differences, but whether they are significant remains to be seen.
> Not necessarily scaling limitations fundamental to the architecture as such, but limitations in our ability to develop sufficiently well developed training texts and strategies across so many problem domains.
I think there are several big issues with that thinking. One is that this constraint is an issue now in large part because GPT doesn't have "memory" or an ability to continue learning. Those two need to be overcome to let it truly scale, but once they are, the game fundamentally changes.
The second is that we're already at a stage where using LLMs to generate and validate training data works well for a whole lot of domains, and that will accelerate, especially when coupled with "plugins" and the ability to capture interactions with real-life users [1]
E.g. a large part of human ability to do maths with any kind of efficiency comes down to rote repetition and generating large sets of simple quizzes for such areas is near trivial if you combine an LLM at tools for it to validate its answers. And unlike with humans where we have to do this effort for billions of humans, once you have an ability to let these models continue learning you make this investment in training once (or once per major LLM effort).
A third is that GPT hasn't even scratched the surface in what is available in digital collections alone. E.g. GPT3 was trained on "only" about 200 million Norwegian words (I don't have data for GPT4). Norwegian is a tiny language - this was 0.1% of GPT3's total corpus. But the Norwegian National Library has 8.5m items, which includes something like 10-20 billion words in books alone, and many tens of billions more in newspapers, magazines and other data. That's one tiny language. We're many generations of LLM's away from even approaching exhausting the already available digital collections alone, and that's before we look at having the models trained on that data generate and judge training data.
[1] https://sharegpt.com/
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Humans in Humans Out: GPT Converging Toward Common Sense in Both Success/Failure
of that conversation. Perhaps something like shareGPT[1] can help?
[1] https://sharegpt.com
What are some alternatives?
langchain - ⥠Building applications with LLMs through composability ⥠[Moved to: https://github.com/langchain-ai/langchain]
ChatGPT - Lightweight package for interacting with ChatGPT's API by OpenAI. Uses reverse engineered official API.
openai-cookbook - Examples and guides for using the OpenAI API
llm-workflow-engine - Power CLI and Workflow manager for LLMs (core package)
BetterChatGPT - An amazing UI for OpenAI's ChatGPT (Website + Windows + MacOS + Linux)
unofficial-chatgpt-api - This repo is unofficial ChatGPT api. It is based on Daniel Gross's WhatsApp GPT
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
openai-python - The official Python library for the OpenAI API
Learn_Prompting - Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community
chatgpt-conversation - Have a conversation with ChatGPT using your voice, and have it talk back.
awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better.