Video

This is very informative.

Well explained informative video.

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

What makes layoffs okay? Understand that AI will catapult success in so many ways, but can AI address alternatives to people getting laid off first?

great content, thank you so much for sharing!!

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

Amazing, had never seen this side of Machine learning/AI models and their applications.

Any chance the google doc can be shared?

It is very apparent that this question was given to him before the call, and he's not coming in blind. Look at him check out a doc on the screen at every single step, which of course, has the framework on PM exercises. This is not an actual mock interview.

you can give me repo or this notebook or not ?

Perhaps it would have helped if Chris would have asked initially, if this is for Web or Mobile App. I felt there was an assumption while he was comparing G - Cal with native calendar of the phone.

Hello I need document you mentioned in video Please provide me

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

Please share a notebook/video with the Collaborative approach for recommendation systems

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That was cool! , Can I just send all the vector embeddings at once to the LLM and then query the model? This will reduce the tokens sent to LLMs., I am currently splitting the PDF into smaller chunks, uploading it to GPT 4o and prompting GPT 4o to consider all the split docs as one doc. So far, I am getting good results for my prompts

It was a wonderful session, I have one question, lets say there is a 600-page document, I want to perform RAG operation with two models 'GPT 4o' and 'OLLAMA-3(local)', in both cases I create my own vector database locally before querying the model, so once I share my vector database, is my data safe (meaning, can anyone else query my context after sharing vdb)

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

I think initially it was told Govt body and then the segment was pointing to dog owners? Felt digressed and couldn't hold the attention.

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

great content, can you improve the sound somehow?!

A few questions/suggestions: 1. Shouldn't the laundry list of proposed metrics be prioritized by some logidc and a North Star metric selected? I did not hear a summary/recommendation. 2. Shouldn't the North Star metric be related or be the same as the NS for FB Feed, which is time spent? The candidate does a good job explaining the value of the reactions to the user, e.g. a less fatiguing browsing experience, but there is no connection between the value and the NS metric. 3. Finally, shouldn't the candidate pick some counter-metrics to ensure that the proposed metrics are not "gamed" or are not obscuring the real picture. I think, the interviewer captured this by suggesting to look at the segments of the users who adopt the reactions feature, in order not to obscure a spike in reactions per post per user coming from a small subset of users. Overall, I think, the interviewer feedback was on the money.

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

I am enjoying this. It has been very terse honing skills in product management and been a medical student. I think I would just stick to this channel and my LinkedIn learning. I would be glad if there any other resources you can share to make the journey easier and merrier.

🎯 Key Takeaways for quick navigation: 00:00 *📝 Introduction to Retrieval-Augmented Generation (RAG)* - Brief introduction to the workshop and RAG. - Explains the motivation for using RAG due to limitations of LLMs. - Overview of how RAG combines LLMs with contextual information to improve responses. 01:06 *🏗️ Architecture of RAG System* - Explanation of RAG system architecture. - Embeddings are stored in a vector database and queried for responses. - Example use case: Financial analysis on SEC 10K forms. 03:44 *📂 Setting Up the RAG System* - Step-by-step setup of the RAG system for the workshop. - Installation of necessary libraries and dependencies. - Initial setup of vector database using ChromaDB and Llama Index. 05:50 *🔄 Chunking Strategy and Embedding Creation* - Simple chunking strategy using paragraph-based chunks. - Discussion on different chunking strategies and their trade-offs. - Initialization of embedding models and storing embeddings in the vector database. 09:38 *💬 Querying the RAG System* - Demonstration of querying the RAG system with financial questions. - Example questions related to Apple's financial health. - Handling queries and generating responses using the RAG setup. 12:01 *📊 Evaluating and Visualizing Results* - Discussion on evaluation metrics for RAG models. - Mention of popular libraries for evaluation. - Brief talk on the visualization of results and the complexity involved. 27:49 *☁️ Comparing Cloud and Local Deployment for LLMs* - Discussion on the differences between running LLMs locally versus on the cloud. - Cloud solutions handle infrastructure and scaling. - Local deployments require managing LLMOps and infrastructure. 28:31 *🔄 Automating Model Testing* - Approaches to automate the testing of model responses. - Using benchmark datasets to evaluate model performance. - Challenges in ensuring consistent confidence in model responses. 30:24 *🔧 Controlling Model Output* - Techniques to control the quality of model outputs. - Adjusting temperature settings to manage response confidence. - Trade-offs between performance and accuracy. 33:22 *🏆 Evaluation Metrics for RAG Models* - Evaluation metrics used to measure RAG model performance. - Libraries like RAGAS provide standard metrics for evaluation. - Importance of testing on diverse datasets for reliable metrics. 37:05 *🔍 Using Existing Vector Databases* - Leveraging existing vector databases for RAG implementation. - Options for integrating RAG with current vector databases. - Benefits of using cloud-based vector databases for scalability. 40:24 *🛡️ Ensuring Data Security* - Strategies to ensure data security when using RAG. - Anonymizing sensitive information before processing. - Using on-premise solutions to maintain data privacy. 44:57 *🏢 Companies Implementing RAG* - Examples of companies effectively implementing RAG. - Mention of Deep Judge in the legal space. - Importance of exploring successful RAG implementations for inspiration. Made with HARPA AI

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

Fantastic!... really helps any AI/ML PM to consider these factors as acceptance criteria.

How do I become a part of this cohort? I am so interested and on the path for the AI Product Management Jobs.

In case you are interested in becoming an AI product manager, check out our AI product manager learning program. It's the most comprehensive program with lots of hands-on live workshops: www.productmanagementexercises.com/ai-product-manager

This video gives me hope. I am in a very similar boat. I am really curious to learn how he got the consulting gig going as a PM. I would really like to begin my consulting firm in the SaaS/AI PM space.

Can you post the collab notebook used in this lesson?

Friday May 10th is the last day to take advantage of joining our upcoming cohort on AI Product Management. Use the link below to apply to enroll www.productmanagementexercises.com/ai-ml-product-manager

This is probably the best overview of AI product management I've come across

Friday May 10th is the last day to take advantage of joining our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇 www.productmanagementexercises.com/ai-ml-product-manager

Do you have free course to provide

May 10th is the last day to take advantage of joining our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇 www.productmanagementexercises.com/ai-ml-product-manager

Monday April 22nd is the last day to take advantage of the 5% discount for our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇👇👇 www.productmanagementexercises.com/ai-product-manager

Monday April 22nd is the last day to take advantage of the 5% discount for our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇👇👇 www.productmanagementexercises.com/ai-product-manager

Monday April 22nd is the last day to take advantage of the 5% discount for our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇👇👇 www.productmanagementexercises.com/ai-product-manager

Monday April 22nd is the last day to take advantage of the 5% discount for our upcoming cohort on AI Product Management. Use the link below to apply to enroll 👇👇👇 www.productmanagementexercises.com/ai-product-manager

Thank You, Priyanka, you articulated bias concepts clear and loud. Your lecture immensely helped me internalize these concepts.

Interesting way of learning through examples

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How about a good summary from the entire session as the speech isn't that clear

1/10 all over the place