4k views and only 2 comments. This is the best TH-cam video I've seen by far on these strategies. Great content - thank you so much for sharing your expertise!
🎯 Key Takeaways for quick navigation: 00:00 📚 Introduction to the topic of emerging architectures for LLM applications. 01:54 🧐 Why focus on LLM architectures. 04:02 📊 Audience poll on LLM use cases. 05:17 🧠 Retrieval Augmented Generation (RAG) as a design pattern. 08:05 💡 Advanced techniques in RAG and architectural considerations. 14:40 📦 Orchestration and addressing complex tasks with LLMs. 23:53 🧩 LLMs in Intermediate Summarization 26:43 📊 Monitoring in LLM Architecture 32:04 🛠️ LLM Agents and Tools 39:05 🔄 Improving LLM Inference Speed 49:26 🛡️ OpenAI's ChatGPT and its relevance in the field, 50:12 🌐 Evolution of ChatGPT and the AI landscape, 51:09 💼 OpenAI's models and their resource allocation, 52:16 🏢 Factors influencing model choice: Engineering, economy, and legal considerations, Made with HARPA AI
I was very pleased to see how well everything was broken down! I was also shook to see a lot of the architecture strategies were things we were already implementing at our company so I'm happy to see we are on the right track 😅
Very informative. Several layers of LLM architectures need to be simplified like this. Maybe a standard for XAI should be developed based on a simplified architectural stack like this for LLMs.
Really informative video. It will be interesting to see how different layers are formed throughout the coming months. Given the complexities of RAG, it'd be interesting to see hosted solutions that can offer competitive pricing on a RAG engine.
![[1hr Talk] Intro to Large Language Models](http://i.ytimg.com/vi/zjkBMFhNj_g/mqdefault.jpg)
This is one of the best webinars I have seen on this topic. Great slides and presenters!
4k views and only 2 comments. This is the best TH-cam video I've seen by far on these strategies. Great content - thank you so much for sharing your expertise!
🎯 Key Takeaways for quick navigation:
00:00 📚 Introduction to the topic of emerging architectures for LLM applications.
01:54 🧐 Why focus on LLM architectures.
04:02 📊 Audience poll on LLM use cases.
05:17 🧠 Retrieval Augmented Generation (RAG) as a design pattern.
08:05 💡 Advanced techniques in RAG and architectural considerations.
14:40 📦 Orchestration and addressing complex tasks with LLMs.
23:53 🧩 LLMs in Intermediate Summarization
26:43 📊 Monitoring in LLM Architecture
32:04 🛠️ LLM Agents and Tools
39:05 🔄 Improving LLM Inference Speed
49:26 🛡️ OpenAI's ChatGPT and its relevance in the field,
50:12 🌐 Evolution of ChatGPT and the AI landscape,
51:09 💼 OpenAI's models and their resource allocation,
52:16 🏢 Factors influencing model choice: Engineering, economy, and legal considerations,
Made with HARPA AI
crazy
This vieo was great! Thank you so much.
I was very pleased to see how well everything was broken down! I was also shook to see a lot of the architecture strategies were things we were already implementing at our company so I'm happy to see we are on the right track 😅
Very interesting, thanks for sharing
Really great presentation.Keep up the good work
Informative video ... Waiting for next video :)
Very informative. Several layers of LLM architectures need to be simplified like this. Maybe a standard for XAI should be developed based on a simplified architectural stack like this for LLMs.
it was awesome, thanks guys, keep up the good work.
Really informative video. It will be interesting to see how different layers are formed throughout the coming months. Given the complexities of RAG, it'd be interesting to see hosted solutions that can offer competitive pricing on a RAG engine.
So informative, looking forward to seeing more
That was very nice, thank you all
Wonderful video, learns a lot, thanks
Nice to see these topics covered, these come up as soon as I was attempting to implement something with llms
I am curious to know what you are trying to implement.
Very informative, thanks!
Wonderful video, learns a lot, thanks. This vieo was great! Thank you so much..
Great talk !
great ideas txs!
Can you make the slides available? I have an issue seeing them and following along.
No problem here you go - go.superwise.ai/hubfs/PDF%20assets/LLM%20Architectures_8.8.2023.pdf
Excellent detailed information thanks, please share slide details,
Thank you!
You can access the slides here - go.superwise.ai/hubfs/PDF%20assets/LLM%20Architectures_8.8.2023.pdf
Great!
Can someone provide an example of how one might introduce time as a factor in the embedding?
It would be in a meta field that you use to filter results, not in the vector embeddings itself.
Honestly. Not huge value for 55 minutes,,,
these videos seldom are.. lol.
Total gibberish in this video