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→ View original post on X — @datachaz

Lessons Learned Building with Large Language Models

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@datachaz

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31 May 2024 10h17

What we learned from a year of building with LLMs. A great read for anyone interested in building things with large language models. → https://
oreilly.com/radar/what-we-
learned-from-a-year-of-building-with-llms-part-i/
… https://
oreilly.com/radar/what-we-
learned-from-a-year-of-building-with-llms-part-i/
…

Using Codestral to Implement Code Changes Efficiently

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31 May 2024 9h31

Prompt codestral to make those changes in the code and it should be able to that.

AI Generates Full Applications from Single Natural Language Prompts

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31 May 2024 8h24

It generates the python code to create a full-fledged application just from a single prompt.

→ View original post on X — @swyx

RAG Retrieval Optimization Through Representation Engineering and Adapters

By

@swyx

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31 May 2024 6h31

took a while to groq but great clear explanation here! an analogy could be that this is @voooooogel style rep eng applied to rag retrieval queries instead of inference outputs? adapters for everything. you could maybe even do adapters for multimodal data! as chameleon style

Awesome LLM Apps with RAG Demonstrations on GitHub

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31 May 2024 4h27

Find all the awesome LLM Apps demo with RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support

Build LLM RAG App Chat PDF Mistral Codestral

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31 May 2024 4h27

Build a LLM app with RAG to Chat with PDF using Mistral AI's Codestral in less than 5 minutes.

Go from a simple english prompt to fully working Python application. pic.twitter.com/5BiAY4vVEd
— Shubham Saboo (@Saboo_Shubham_) 31 mai 2024

Build a LLM app with RAG to Chat with PDF using Mistral AI's Codestral in less than 5 minutes. Go from a simple english prompt to fully working Python application.