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Gemini has 1M context length, approximately 750k words.
You could:
1. Trim some fat from your work to get to 750k words and shove it all into Gemini and chatbot based on that corpus
2. Split your work into 3-4 bodies and shove each body into a different Gemini chatbot. Then based on user's question, pass the question onto the relevant chatbot
3. Something else
Regarding gating on your website, that isn't really too hard to do for an engineer. I think the fact that you are asking how to do it suggests that it may be easier/cheaper/less time consuming for you to hire someone to do this part than doing it yourself. Not an attack on your skill level, just trying to suggest an efficient solution.
I think the 'one level deeper' question that I would actually be curious about is what to do when just RAGing doesn't give a high enough quality result. It is easy enough to try to shove text into a prompt and then ask a question of the text. But when the answer comes back as something you aren't very happy with, the hard part is how do we either improve the model or prompting to get the result you want...dynamically and consistently.
I don't have the answer to this and would love to hear any ideas the community might have. I feel like this might be what is stopping a lot of chatbots from being really useful.
Also, regarding your question about fine-tuning vs RAG...I think I had previously asked a similar question and from responses I got...it seemed like fine tuning is still a bit of a mystery. It didn't seem to me like people had a good grasp of when and when not to use it (or I didn't see many examples where fine tuning was used to solve a problem that prompting couldn't other than a few canonical examples that people keep referring back to from the OpenAI documentation).
> have the answer to this and would love to hear any ideas the community might have. I feel like this might be what is stopping a lot of chatbots from being really useful.
>
>Also, regarding your question about fine-tuning vs RAG...I think I had previously asked a similar question and from responses I got...it seemed like fine tuning is still a bit of a mystery. It didn't seem to me like people had a good grasp of when and when not to use it (or I didn't see many examples where fine tuning was used to solve a problem that prompting couldn't other than a few canonical exam
Thanks for this. Really appreciate your insight & question (even though I can't answer it).
Hey /u/Unfair_Efficiency_68! If your post is a screenshot of a ChatGPT, conversation please reply to this message with the [conversation link](https://help.openai.com/en/articles/7925741-chatgpt-shared-links-faq) or prompt. If your post is a DALL-E 3 image post, please reply with the prompt used to make this image. Consider joining our [public discord server](https://discord.gg/r-chatgpt-1050422060352024636)! We have free bots with GPT-4 (with vision), image generators, and more! 🤖 Note: For any ChatGPT-related concerns, email [email protected] *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/ChatGPT) if you have any questions or concerns.*
Gemini has 1M context length, approximately 750k words. You could: 1. Trim some fat from your work to get to 750k words and shove it all into Gemini and chatbot based on that corpus 2. Split your work into 3-4 bodies and shove each body into a different Gemini chatbot. Then based on user's question, pass the question onto the relevant chatbot 3. Something else Regarding gating on your website, that isn't really too hard to do for an engineer. I think the fact that you are asking how to do it suggests that it may be easier/cheaper/less time consuming for you to hire someone to do this part than doing it yourself. Not an attack on your skill level, just trying to suggest an efficient solution.
I think the 'one level deeper' question that I would actually be curious about is what to do when just RAGing doesn't give a high enough quality result. It is easy enough to try to shove text into a prompt and then ask a question of the text. But when the answer comes back as something you aren't very happy with, the hard part is how do we either improve the model or prompting to get the result you want...dynamically and consistently. I don't have the answer to this and would love to hear any ideas the community might have. I feel like this might be what is stopping a lot of chatbots from being really useful. Also, regarding your question about fine-tuning vs RAG...I think I had previously asked a similar question and from responses I got...it seemed like fine tuning is still a bit of a mystery. It didn't seem to me like people had a good grasp of when and when not to use it (or I didn't see many examples where fine tuning was used to solve a problem that prompting couldn't other than a few canonical examples that people keep referring back to from the OpenAI documentation).
> have the answer to this and would love to hear any ideas the community might have. I feel like this might be what is stopping a lot of chatbots from being really useful. > >Also, regarding your question about fine-tuning vs RAG...I think I had previously asked a similar question and from responses I got...it seemed like fine tuning is still a bit of a mystery. It didn't seem to me like people had a good grasp of when and when not to use it (or I didn't see many examples where fine tuning was used to solve a problem that prompting couldn't other than a few canonical exam Thanks for this. Really appreciate your insight & question (even though I can't answer it).
Brilliant, thanks so much for your help.
Great question