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it was awesome, thanks guys, keep up the good work.

These are some of the best videos I've seen on this topic.

Great video, was great to learn about all the things that go into putting a model into action.

Thanks for this presentation. It was really insightful.

Great video- really insightful. I will share this on LinkedIn, as I think more people need to see it!

Great!

great

great ideas txs!

🎯 Key Takeaways for quick navigation: 00:00 🤖 *Introduction to the topic of emerging architectures for LLM applications* - Overview of the importance of understanding the architectures for implementing LLMs. - Introduction of the speakers from Tensor Ops and Superwise. - Contextual explanation of Tensor Ops as an AI consultancy service and Superwise's role in deploying LLMs in production environments. 01:38 🏗️ *Overview of the agenda and the concept of "Emerging Architectures for LLM Applications"* - Naming inspiration from a16z's blog post "Emerging Architectures for LLM Applications." - Emphasis on exploring concepts introduced by a16z but with unique insights and use cases. - The need to address limitations faced when deploying LLMs in real-world applications. 02:49 🌐 *Why discuss architectures for LLMs and the limitations faced in production* - Addressing the hype vs. reality of AI taking over jobs and the actual limitations faced in deploying LLMs. - Mention of limitations, such as the model's limited context window and biases, leading to the need for architectural considerations. - Introduction of a poll to gather insights on the use cases the audience is exploring with LLMs. 05:17 🧠 *Retrieval Augmented Generation: Design pattern and common use case* - Introduction to the design pattern called "Retrieval Augmented Generation" (RAG). - Explanation of how RAG addresses the limitations of LLMs by combining retrieval systems with LLMs. - Highlighting the importance of using a vector database as part of the retrieval system. 08:48 🔍 *Limitations and considerations in designing Retrieval Augmented Generation systems* - Overview of limitations faced in LLMs, such as limited context windows and biases. - Introduction to the use of vector databases and potential issues with their applicability. - Advanced techniques like time component addition and using smaller llms for post-processing to improve accuracy. 13:17 🛠️ *Components and steps in developing Retrieval Augmented Generation systems* - Overview of the four main steps in developing RAG systems: data ingestion, pre-processing, retrieval strategy, and efficient document retrieval. - Emphasis on the need for efficient orchestration to overcome limitations in LLMs. - Introduction of advanced RAG architectures for improved accuracy. 15:06 ⚙️ *Advanced Retrieval Augmented Generation architectures for improved accuracy* - Explanation of strategies like time component addition, relevance reorganization, contextual compression, and self-querying. - Highlighting the importance of context in improving the efficiency and relevance of retrieved documents. - The role of llms in post-processing to enhance the quality of retrieved information. 18:00 🔄 *Iterative prompt engineering and the use of FLAIR technique* - Acknowledgment of the importance of prompt engineering for better results in LLMs. - Introduction to the FLAIR technique for refining prompts and improving confidence in llm responses. - The iterative process of refining prompts, retrieving data, and generating responses to achieve better performance. 20:28 🤔 *Tackling complex tasks beyond the capabilities of a single LLM* - Transition from traditional MapReduce-like strategies to handling more complex tasks in LLMs. - Introduction of the challenge of generating content or summaries for massive datasets. - The need for solutions beyond simple aggregation for tasks that cannot be handled by a single LLM. 23:03 🚀 *Orchestrating multiple LLMs for complex tasks* - Overview of the challenges in orchestrating multiple LLMs for complex tasks. - Introduction to the need for efficient aggregation strategies for tasks like content generation. - The complexity of tasks requiring collaboration among multiple LLMs for more effective outcomes. 23:29 📚 *Overview of MapReduce in LLM Applications* - Explanation of the inspiration behind MapReduce in language models. - Discussion on dividing and processing text data in chunks for intermediate summarization. - Introduction to emerging merging algorithms and their parallel to MapReduce. 26:43 📊 *Monitoring in LLM Architecture* - Differentiation between LLM evaluation and monitoring. - Explanation of the evaluation phase and benchmarks in language models. - Focus on monitoring, highlighting the key differences in approach compared to classical machine learning systems. 27:49 🔄 *Phases of Monitoring in LLM* - Overview of monitoring phases: User Query, Retrieval Process, Model Response, and User Feedback. - Specific metrics for monitoring user queries, including sentiment score, topic classification, and injection detection. - Metrics and considerations for monitoring the retrieval process, emphasizing relevance and ranking. 34:24 🚀 *Metrics for Monitoring Model Responses* - Introduction to monitoring model responses, focusing on counting refusals and analyzing similarity drift. - Considerations for detecting personal identifiable information (PII) in responses. - Emphasis on monitoring user feedback, distinguishing between explicit and implicit feedback and its valuable role. 37:14 🔧 *Agent Architecture in LLM* - Explanation of agent architecture and its experimental nature. - Introduction to the React framework and its role in decomposing tasks for language models. - Discussion on the reflection framework and its impact on accuracy improvement. 39:35 🛠️ *Tools in Agent Architecture* - Overview of tools in agent architecture as functions accepting strings for specific tasks. - Examples of tools, such as Python interpreters, Google searching, and data fetching, as extensions of the language model's capabilities. - Highlighting the potential of agents to decide when to use these tools dynamically. 43:17 🚄 *Techniques to Speed Up LLM Inference* - Discussion on techniques to enhance LLM inference speed, including token limit adjustments. - Considerations for caching, fine-tuning, and reducing the size of language models. - Emphasis on the complexity of addressing inference speed, involving various factors beyond model size. 47:27 ⚠️ *Safeguarding LLM Outputs* - Exploration of strategies for preventing out-of-context or nonsensical results from language models. - Consideration of the role of prompt engineering and training in mitigating undesired outputs. - Suggestion of using additional language models to filter and refine outputs for improved context awareness. 48:29 🌐 *AI Safety and Fine-Tuning LLMs* - Fine-tuning LLMs for specific safety metrics. - AI safety companies using smaller models for safety validation. 49:12 🌱 *Gardening Concept in LLMs* - Post-processing step after model response for safety. - Filtering out offensive or PII content and replacing with a generic response. 49:57 🔄 *Evolution of ChatGPT and Relevance* - Discussion on the changing relevance of ChatGPT. - OpenAI's early edge, increasing competition with other models. - Gap closing, factors beyond models' quality influencing decisions. 52:30 🌐 *Closing Gap and Considerations* - Closing gap in model quality. - Importance of engineering systems and support behind models. - Decision-making factors: price, availability, privacy, legal considerations. Made with HARPA AI

@superwiseai please share the presentation

Total gibberish in this video

Great talk, your webinars are a wonderful source of information :)

Informative video ... Waiting for next video :)

Hm… the first speaker made it sound like you couldn’t use context outside of the user input without prompt chaining. This is not true. Otherwise, loved the talk !

Great talk !

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, thanks!

That was very nice, thank you all

This series has been insightful. Thanks for uploading

🎯 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

So informative, looking forward to seeing more

Can someone provide an example of how one might introduce time as a factor in the embedding?

Thank you for your insights. I see there are no comments and not that many views. But please keep posting. This is changing the world.

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.

Wonderful video, learns a lot, thanks. This vieo was great! Thank you so much..

Excellent detailed information thanks, please share slide details,

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.

Honestly. Not huge value for 55 minutes,,,

Really great presentation.Keep up the good work

Can you make the slides available? I have an issue seeing them and following along.

This vieo was great! Thank you so much.

Very interesting, thanks for sharing

Great sharing, that's just what I am looking for

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

4k views and only 2 comments. This is the best RUclips video I've seen by far on these strategies. Great content - thank you so much for sharing your expertise!

This is one of the best webinars I have seen on this topic. Great slides and presenters!

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