GirlfriendGPT VS prompt-engineering

While Poe from Quora is extempted from stripe restricted businesses, startups like GirlfriendGPT get bullied! The functionality of both AI chat engines is the same.

Geeesh I’m kinda new to this whole coding, making your own GPT type stuff lol and if you ask me so far so good I can definitely follow along and plug in codes and prompts just to make your basic chatbot lol. But! Now I’m trying to make the GirlfriendGpt & I’ve found the code/template on GitHub https://github.com/EniasCailliau/GirlfriendGPT only thing is I don’t know how to code it without watching someone on YouTube walk me through the steps. Is there anyone or anyway I can get this coded to create the AI girlfriend I want????

BTW, here is the service:)))

💻 Check GitHub to see the service. Would be glad to see your results! Maybe I’ll test it in the future, too:))

Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0 (https://github.com/EniasCailliau/GirlfriendGPT)

Some guy has already made GirlfriendGPT, which allows you to define a personality, and it can also send you AI generated selfies. You could probably define an RC personality and host this things in the AWS free tier.

Here you go: https://github.com/EniasCailliau/GirlfriendGPT

An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).

You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react

Well, if developers get replaced by AI, then who are the managers going to manage :). I personally don't think AI is just going to replace us. The way we work will continue to change as new AI tools come out. I'm taking time to tinker with new tools and seeing how others do as well (e.g., I found Brex's tips and tricks for working with LLMs very insightful: https://github.com/brexhq/prompt-engineering).

I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.

These are good straight-to-the-point guides:

- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering

- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...

- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...

- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...

tl;dr:

Brex's Prompt Engineering Guide (https://github.com/brexhq/prompt-engineering)