Everyone’s using ChatGPT, Grok, and Gemini. But 99% are stuck in beginner mode. Here are 10 advanced prompting techniques (with copy-paste templates) to level up your AI skills today:
LLMS
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10 Advanced Prompting Techniques to Level Up AI Skills
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Everyone’s using ChatGPT, Grok, and Gemini. But 99% are stuck in beginner mode. Here are 10 advanced prompting techniques (with copy-paste templates) to level up your AI skills today:
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Complete LLM Course: Hands-on Learning with Notebooks
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9. LLM Course: The best hands-on course to learn Large Language Models with roadmaps and Colab notebooks! The LLM Course covers end-to-end LLM application lifecycle from design to deployment. – Github Repo:
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Fine-tuning Models: Cost-Benefit Analysis vs Paid Solutions
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I think it's slow, expensive, very hard to get right and rarely gives you a result that's better than if you paid for one of the more expensive models – and the more expensive models are mostly cheap enough that it's not worth investing the time and money in a fine tuning project
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Vercel’s Fine-Tuned Production AI Model Strategy
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OK v0 does looks like that rare instance of a company writing about fine-tuned models that they use in production – in their case the "vercel-autofixer-01" model Looks like most of their stuff still uses off-the-shelf Claude though (Cc @0xSMW
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Non-AI Teams Fine-Tuning LLMs for Specific Problem Solving
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… trained by OpenAI themselves – that still doesn't work for me as a success story for a non-AI lab team fine tuning an LLM to solve a specific problem
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Codex to GPT: Code Model Evolution Explained
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Not as far as I know – originally it was powered by Codex, a code specialized model released by OpenAI, then later it switched to other off-the-shelf GPTs
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Fine-tuning commercial : où sont les success stories ?
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It may be demonstrated in academic papers, but where are the commercial success stories? I want to hear from companies that used fine tuning to solve the kinds of problems I face in my own work I've been trying to find convincing stories around this for a couple of years now
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Fine-tuning adoption challenges lack compelling success stories
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I think a bigger problem is the almost complete lack of fine tuning success stories – it's still really hard to find convincing stories of teams that used fine tuning for anything more interesting than "we got a cheap model to classify things slightly better"
