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dude plz make a detailed video on how to implement rag with langchain and crewai and plz keep it opensourced like if you can use opensourced llms , embedding models

Within very little time, it'll be driven by natural language.

CORRECTION: The example at 14:00 isn't technically "few shot", as there are no specific examples in the prompt, it's simply giving the LLM more context to produce more relevant answers.

Your videos might be somewhat unpolished, lack sophistication but they cover such interesting topics and your enthusiasm and genuiness more than make up for it I love watching your stuff, to the point where I actually get off my arse and try stuff out Keep up the great work

Ultimately these models predict the next word in a sequence - they essentially "think out aloud". One thing a lot of successful prompting techniques like the ones here do is they get the LLM to think about the problem before coming up with a final answer.

Very appreciative of your videos, I'm learning a lot. Thank you!

So true David, I have myself to blame for most poor output due to poor prompts. But it if improve my prompt the output is 10x better.

I speak fluent ChatGPT

Thank you for this video. As someone brand-new to ChatGpt, I appreciate the way you explain things and simplify into plain English.

I write prompts at the top of word docs that contain knowledge I add to a custom GPT. So the word doc has a heading of "GPT Prompt:" and I break down an instruction that starts with "If you are asked to....then follow these rules and refer to this document". This essentially makes the document a macro where I can then just say something like "write a summary of this transcript" and it follows very specific rules and examples in a word doc, reading it's prompt and examples provided.

I would love another video on how you actually use tree of thought method. It looks interesting, but sounds like an agent only function. Also when it generates multiple different branches, how does it know which is the best to present... or does a human have to go through all of the responses to find the gems?

Amazing collection of all relevant advice. How can I get my hands on the slides for this video?

To be honest it is one of good videos about prompting , short and also complete

4:42 In multiple choice Llm pick one & hallucinate based on that.

I use my own prompts, the result is amazing.

In my experience with OpenAI, when adding multiple entries to fine-tune the result, repeating what you want it to do can lead to undesirable results. At the same time, if you don't remind it of certain things, it acts like Dori, the fish, and forgets essential things. This is especially true when coding.

Thanks for this, it was very helpful. I'm curious what your take is on higher level libraries like crewai, which seem to go against your strategies of clear definition and control here. Higher level libraries purport to make the process easier, but in practice all it seems to do is obfuscate the prompts and make it harder to get things working the way you want. Many times I've just scrapped it and gone back to a simpler langchain model that does basically the same thing as delegation. (Admittedly, I'm still a novice).

I got Hermes models pretty locked in. It's to the point that it's become another reason I prefer them.

Give it more parameters to abide by or control

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The fact we need prompt engineering instead of just chatting shows how dumb those systems really are.