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OpenAI just dropped their Prompt Engineering guide. Here are 6 strategies they recommend for getting better results from LLMs:

Open AI released a great Prompt Engineering guide (link in comments). Here is a summary of their 6 strategies for getting better results when prompting GPT-4 https://t.co/QiBASsbOHD

🌳 Galileo LLM Studio

Algorithm-powered LLMOps Platform

Find the best prompt, inspect data errors while fine-tuning, monitor LLM outputs in real-time. All in one powerful, collaborative platform.

My new favourite AI trend. Incorporate your text or image into any scene. e.g. here's the Linear logo and name as part of various environments. Steps on how to do it in thread ↓

2-5x faster 50% less memory local LLM finetuning

• Manual autograd engine - hand derived backprop steps.


• 2x to 5x faster than QLoRA. 50% less memory usage.


• All kernels written in OpenAI's Triton language.


• 0% loss in accuracy - no approximation methods - all exact.


• No change of hardware necessary. Supports NVIDIA GPUs since 2018+. Minimum CUDA Compute Cap

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With many 🧩 dropping recently, a more complete picture is emerging of LLMs not as a chatbot, but the kernel process of a new Operating System. E.g. today it orchestrates: - Input & Output across modalities (text, audio, vision) - Code interpreter, ability to write & run… Show more