dspy
awesome-chatgpt-prompts
dspy | awesome-chatgpt-prompts | |
---|---|---|
22 | 157 | |
10,820 | 104,070 | |
17.5% | - | |
9.9 | 7.0 | |
6 days ago | 6 days ago | |
Python | HTML | |
MIT License | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dspy
-
Computer Vision Meetup: Develop a Legal Search Application from Scratch using Milvus and DSPy!
Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.
-
Pydantic Logfire
I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)
Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)
Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
-
Princeton group open sources "SWE-agent", with 12.3% fix rate for GitHub issues
DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy
Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.
-
Winner of the SF Mistral AI Hackathon: Automated Test Driven Prompting
Isn’t this just a very naive implementation of what DsPY does?
https://github.com/stanfordnlp/dspy
I don’t understand what is exceptional here.
-
Show HN: Fructose, LLM calls as strongly typed functions
Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)
Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.
-
AI Prompt Engineering Is Dead
I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?
-
Ask HN: Are you using a GPT to prompt-engineer another GPT?
You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy
- SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
- FLaNK Stack Weekly for 12 September 2023
awesome-chatgpt-prompts
- Top ChatGPT prompts I could find with ranking system
- FLaNK Stack Weekly 12 February 2024
-
🌌 5 Open-Source GPT Wrappers to Boost Your AI Experience 🎁
Aside from the built-in prompts powered by awesome-chatgpt-prompts (Are you an ETH dev, a financial analyst, or a personal trainer today?), you can also create, share and debug your chat tools with prompt templates.
- Aprimorando as respostas do ChatGPT com prompts estratégicos
-
Ask HN: Daily practices for building AI/ML skills?
I've found the following resources helpful:
- 15 Rules For Crafting Effective GPT Chat Prompts (https://expandi.io/blog/chat-gpt-rules/)
- Awesome ChatGPT Prompts (https://github.com/f/awesome-chatgpt-prompts)
For more resources of like nature, you can search for "mega prompt".
-
Prompt writing communities
Someone assembled an adhoc page in Github that is amassing quite a large library of prompt ideas [Github]
-
Ask HN: Collection of best GPT-4 prompts?
I like to use PromptLayer for this. But you could easily set up a simple CRUD web app to track prompts/average completion token # length, different variations.
There is also awesome-chatgpt-prompts (https://github.com/f/awesome-chatgpt-prompts) which has some interesting ones. What are you looking for?
- Supercharge your writing with ChatGPT prompts
-
Introducing YourChat: A multi-platform LLM chat client that supports the APIs of text-generation-webui and llama.cpp.
* Built-In Prompts: Channel creativity using integrated prompts sourced from github.com/f/awesome-chatgpt-prompts.
-
Yet another ChatGPT generated workout... but modified.
So, I jumped into the ChatGPT fitness wagon to generate a New And Improved® workout that will have a mix of bodybuilding and calisthenics. I used a pre-made prompt to generate a PPL+FB and specified things like fitness leve, equipment, schedules, etc. in order to make if fit my current status. From there I made it fit some of my needs and chose some exercises that I wanted to do every day: wrist and core.
What are some alternatives?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
ChatGPT-pdf - A Chrome extension for downloading your ChatGPT history to PNG, PDF or a sharable link
open-interpreter - A natural language interface for computers
gpt-prompts-cli - CLI for selecting or defining prompts to use with the ChatGPT chatbot, which retrieves the prompts from the awesome-chatgpt-prompts repository.
playground - Play with neural networks!
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
MLflow - Open source platform for the machine learning lifecycle
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
FastMJPG - FastMJPG is a command line tool for capturing, sending, receiving, rendering, piping, and recording MJPG video with extremely low latency. It is optimized for running on constrained hardware and battery powered devices.
llm-workflow-engine - Power CLI and Workflow manager for LLMs (core package)
prompt-engine-py - A utility library for creating and maintaining prompts for Large Language Models
chatgpt-google-extension - A browser extension that enhance search engines with ChatGPT