Fine Tuning ChatGPT is a Waste of Your Time 

Very good explanation and excellent thinking however the problem is that context Windows or not normally big enough to take all the data. This is why fine tuning is an important part of the mix. The correct usage is a balance between long-term data going into fine tuning and short-term data going into RAG. There will soon be a type of job specifically around the sort of data architecture.

We had a well crafted GPT4 prompt with many tests covering our desired outputs. We took gpt35 and fine tuned it and now it's performing the same. Worked well for our use case!

Excellent description of the challenges in fine tuning AI models! You got yourself a new subscriber 🎉

Relaying what works and what doesn't is highly valuable. Too few people share their experience. Thank You Training/Fine-tuning is a very delicate process, it has to be done really well to get really good results. Moreover it's not a well understood process - new discoveries are constantly being made, even at the highest levels of research.

Waow it's qualitative! You won another sub :) soon you ll be big, I can see that, continue to work hard

Nice video dude. What is that app you are using to visualize your message.

Great Video! What is the whiteboard app that you are using?

I wonder how performance of RAGs will vary with integrating generative and retrieval processes. Seems like it would be difficult to optimise, plus more expensive computationally. Definitely the way forward though

I am very new to this AI field, thank you very much for explaining in simple terms !

Loved this explanation. Subscribed.

5:22 When you started talking about the context window problem, I did think about indexing. I suppose an AI is a sort of an index but it is more dynamic than a traditional database. Setting up a session database would effectively solve the context issue.

Thank you. Your clear explanation of RAG was very useful 👍

I would like to get your advice for creating conversational chatbot. Do RAG or Finetune be suitable because we have a CourtLAW based dataset that contains 1000's of PDF which is unstructured dataset of paragraphs?

Can the content for training be collected from ChatGPT-4? For example, after chatting with ChatGPT-4, can the desired content be filtered and integrated into ChatGPT-3.5 for fine-tuning? Is this approach feasible and effective? Are there any considerations to keep in mind?

Would that be different with the recently introduced custom-gpts which allow you to personalize your model based on your specific instructions and provide it with your own contextual documents for reference?

why, why so tiny amount of subscribers. Very much needed approach to problems, to tell 'wait a minute... here are the stones on the road"

So, if I am correct, you suggest adding a value to the jason object, which is a chunk of data, sending with other set of data for fine-tuning the gpt model like questions and answers, am I right or does it require a different process?

Thank you for making this video. I remember I talked to my friends about a similar concept a few months ago, now I finally know I was not alone! RAG seems like the thing most AI services should have by default.

Currently I am planning and testing about a project which will rely heavily on RAG and I think I will have to also consider fine-tuning, becasue of the way I need the model to format, reference and present information from multiple documents. Still wrapping my head around how to produce the training data, but at the moment my impression is that, at least in my case study (a specialized and niche knowledge base about music and musical research), even RAG requires quite a bit of work to fragment the documents in ways that guarantee reliable retrieval.

I'm not an expert in AI topics, but I really do think the only thing we need is an AI that can just understand and it's just RAG on everything else. Great and insightful video!

🎯 Key Takeaways for quick navigation: 00:00 🤖 *Understanding Fine Tuning in AI* - Fine tuning is a technique to customize AI models, gaining popularity in the AI community. - Major AI companies like OpenAI and AWS focus on making fine tuning more accessible. - The appeal of fine tuning arises from addressing the limitations of AI models, particularly in handling context and relevant information. 02:35 🧠 *Challenges of Fine Tuning and Overtraining* - Defining training data for fine tuning is challenging due to the difficulty in understanding what the model lacks. - Overtraining poses a significant challenge, making the model rigid and less adaptable to changes. - The need for a representative set of data that mirrors real-world scenarios to avoid overtraining pitfalls. 05:19 🔄 *RAG (Retrieval Augmented Generation) as an Alternative* - Retrieval Augmented Generation offers a more flexible approach by breaking information into smaller, manageable pieces. - Using smaller chunks allows for better management of context window problems in AI. - Updating and modifying information chunks becomes easier compared to the fixed nature of fine-tuned models. 06:51 🔐 *Security Concerns in Fine Tuning and RAG* - Security issues arise in fine tuning as users can extract data about the training process and model's responses. - Retrieval Augmented Generation provides better control over which documents go to specific users, enhancing security. - The ability to control and restrict the knowledge base of AI systems based on user requirements. 08:21 🌐 *Future Possibilities with Retrieval Augmented Generation* - Retrieval Augmented Generation opens up diverse possibilities, such as developing autonomous agents with brain-like patterns. - The potential for AI systems to perceive, plan, reflect, and act based on stored details about their environment. - An exploration of the vast capabilities within the space of Retrieval Augmented Generation compared to the limitations of fine tuning. Made with HARPA AI

Training augments behaviour, RAG augments information - they are two different things.

Solid work! Thanks!

I'm far from an expert, but I think at least part of the challenge is when people think fine-tuning is for giving the LLM more DATA; increasing it's knowledge base. That's not what fine tuning is for. It's for customizing the WAY it responds. It's more of a style guide than a knowledge store.

Good explanation. However it looks like these two techniques are not mutually exclusive, e.g. it could still be valuable to finetune a model to improve processing of RAG generations without any specific data, while RAG mechanism supplying all the data for each specific generation

thanks for the insight

Am I the only one here surprised to find out that Jack Black is an AI expert?

Is it a waste of time for novelist too?

🎯 Key Takeaways for quick navigation: 00:00 🎯 *Fine-tuning Overview* - Fine-tuning is a technique to personalize AI models. - It's data-intensive and currently a popular trend in the AI community. - Major AI companies, including OpenAI, are emphasizing fine-tuning. 01:01 🤔 *Why Fine-Tune?* - Fine-tuning addresses limitations in AI's memory space and context windows. - Challenges arise when context exceeds the model's memory, leading to information loss. - AI enthusiasts and companies advocate fine-tuning for more personalized responses. 02:35 ⚠️ *Challenges of Fine-Tuning* - Defining relevant training data is complex, considering unknowns in the model's knowledge. - Overtraining is a significant challenge, leading to rigid responses and missing diverse solutions. - Difficulty in determining what the model lacks in knowledge and how to supplement it. 05:19 🔄 *RAG (Retrieval Augmented Generation)* - RAG breaks down related data into manageable chunks, overcoming context window issues. - It enables searching for specific chunks relevant to the question, improving answer quality. - RAG allows continuous updates to data chunks, providing flexibility compared to fine-tuning. 06:51 🛡️ *Security Considerations* - Fine-tuning and AI interactions may expose proprietary information and data vulnerabilities. - RAG offers stronger control over which documents are sent to specific users, enhancing security. - The ability to control data distribution to users provides additional security benefits. 08:21 🌐 *Future Possibilities of RAG* - RAG opens up exciting possibilities, such as developing autonomous agents with perception and planning capabilities. - The potential for optimizing RAG for various situations makes it a promising area. - RAG's flexibility and adaptability make it a more compelling option compared to fine-tuning. 09:18 🎙️ *Conclusion and Call to Action* - RAG offers more potential than fine-tuning, especially in terms of data curation and understanding. - A glimpse into the fascinating space of RAG and its diverse applications. - Encouragement to follow Stable Discussion for more insights and discussions on AI. Made with HARPA AI

This video and opinion is fairly incorrect in regards to fine-tuning. Especially, fine-tuning can be utilized in any deep learning hyperparameters (i.e. GenAI, Discriminative AI, BERT, NLP) with any data set. Self supervision, supervised, to reinforcement learning just to name a few use cases of algorithms to solve a problem. RU-vids fine-tuning in their algorithm made me stumble upon this video. Highly recommended re-evaluation of this video to save folks from misunderstanding.

I would say you are actually looking for Embeddings. You can set up a database with Embeddings based on our specific data which will be checked for similarities. The matches would then be used to create the context for the completions api. Fine tuning is more to modify the way how it answers. This was my understanding.

Sorry but why then it's a waste of time? It wasn't clear or finally mention as far as I've listened

"you to curate data to feed AI"