Hey everyone, thank you so much for watching the 77th Weaviate Podcast on RAGAS, featuring Jithin James, Shahul ES, and Erika Cardenas! RAGAS is one of the hottest rising startups in Retrieval-Augmented Generation! RAGAS began it's journey with the RAGAS score, a matrix of evaluations for generation and retrieval. Generation evaluated on Faithfulness (is the response grounded in the context) as well as Relevancy (is the response useful). Retrieval is then evaluated on Precision (How many of the search results are relevant to the question?) and Recall (How many of the relevant search results are captured in the retrieved results?). Now, the super novel thing about this is that an LLM is used to determine these metrics. So we circumvent painstaking manual labeling effort with the RAGAS score! This podcast dives into the development of the RAGAS score as well as how RAG application builders should think about the knobs to tune for optimizing their RAGAS score: embedding models, chunking strategies, hybrid search tuning, rerankers, ... ?!? We also discussed tons of exciting directions for the future such as fine-tuning smaller LLMs for these metrics, agents that use tuning APIs, and long context RAG!

Hey everyone, I am SUPER excited to present our 76th Weaviate Podcast featuring Patrick Lewis, an NLP Research Scientist at Cohere! Patrick has had an absolutely massive impact on Natural Language Processing with AI and Deep Learning! Especially notable for the current climate in AI and Weaviate is that Patrick is the lead author of the original "Retrieval-Augmented Generation" paper!! Patrick has contributed to many other profoundly impactful papers in the space as well such as DPR, Atlas, Task-Aware Retrieval with Instruction, and many many others! This was such an illuminating conversation, here is a quick overview of the chapters in the podcast!

Hey everyone! Thank you so much for watching the 75th Weaviate Podcast featuring Tanmay Chopra! The podcast details Tanmay's incredible career in Machine Learning from Tik Tok to Neeva and now building his own startup, Emissary! Tanmay shared some amazing insights into Search AI such as how to process Temporal Queries, how to think about diversity in Retrieval, and Query Recommendation products! We then dove into the opportunity Tanmay sees in fine-tuning LLMs and knowledge distillation that motivated Tanmay to build Emissary! I thought Tanmay's analogy of GPT-4 to 3D printers was really interesting, tons of great nuggets in here! I really hope you enjoy the podcast, as always more than happy to answer any questions or discuss any ideas with you related to the content in the podcast!

Hey everyone! Thank you so much for watching the 74th Weaviate Podcast feature Simba Khadder, the CEO and Co-Founder of FeatureForm! To begin, "features" broadly describe the inputs to machine learning models that they use to produce outputs, or predictions. Feature stores orchestrate the construction of features, whether that be transformations for tabular machine learning models such as XGBoost, to chunking for vector embedding inference, and now features for LLM inference in RAG. Right out of the gate, Simba really opened my eyes to the role that feature engineering plays in RAG. Further touching on this at the very end under the "Exciting future for RAG with Features" chapter, Simba further describes how we can use more advanced features to provide better context to LLMs. In addition to these insights on RAG, there are so many nuggets in the podcast, Simba is a world class professional when it comes to building distributed systems, production scale recommendation systems, and more! I learned so much from chatting with Simba, I hope you enjoy listening to the podcast! As always we are more than happy to answer any questions or discuss any ideas you have about the content in the podcast!

Hey everyone! I am SUPER excited to publish our 73rd Weaviate Podcast with Charles Packer, the lead author of MemGPT at UC Berkeley! MemGPT presents the "Operating System for LLMs", an incredibly exciting idea to explicitly prompt the LLM with the information that it has a limited context window and give it memory management tools to behave accordingly! This was such a fun discussion with Charles diving into all things related to the paper! I hope you enjoy the podcast!!

Hey everyone! Thank you so much for watching the 72nd episode of the Weaviate Podcast with Madelon Hulsebos!! Madelon is one of the world's experts on Machine Learning with Tables and Tabular-Structured Data, this was such an eye-opening conversation! We discussed all sorts of topics from the relationship of tabular data and embeddings, to searching through tables, semantic joins, more complex Text-to-SQL, using machine learning for query execution, using tabular data in search and recommendation reranking, and many more! This was easily one of the most knowledge packed episodes of the Weaviate podcast so far, please don't hesitate to leave any questions or ideas you have related to the content discussed!

Hey everyone! Thank you so much for watching the 71st Weaviate Podcast with Vibs Abhishek! Vibs is the CEO and Founder of Alltius AI, as well as a professor at UC Irvine business school! In order to tame the somewhat chaotic emerging landscape of RAG and LLM applications, Alltius has settled on 3 core pillars of Knowledge, Skills, and Deployment Channels! Vibs further explained how he sees the distinction between Assistants and Agents and many more topics important to Enterprise deployment of RAG applications such as reducing hallucinations and employing classifiers to route skills and knowledge sources! I learned so much from this conversation, I hope you enjoy the podcast!

Thank you so much for watching our paper summary video on MemGPT! MemGPT is a super exciting new work bridging together concepts in how Operating Systems manage memory and LLMs!

Hey everyone! Thank you so much for watching the 70th episode of the Weaviate podcast with Neum AI CTO and Co-Founder Kevin Cohen! I first met Kevin when he was debugging an issue with his distributed node utilization and have since learned so much from him about how he sees the space of Data Ingestion, also commonly referenced as ETL for LLMs! There are so many interesting parts to this from the general flow of data connectors, chunkers and metadata extractors, embedding inference, and the last leg of the mile of importing the vectors to a Vector DB such as Weaviate! I really loved how Kevin broke down the distributed messaging queue and system design for orchestrating data ingestion at massive scale such as dealing with failures and optimizing the infrastructure as code setup. We also discussed things like new use cases with quadrillion scale vector indexes and the role of knowledge graphs in all this! I really hope you enjoy the podcast, please check out this amazing article below from Neum AI!

Hey everyone! Thank you so much for watching the 69th episode of the Weaviate Podcast featuring Charles Pierse from Tactic! Tactic has recently launched their new Tactic Generate project, an incredible UI for conducting research across multiple documents. I think there is a massive opportunity to pair these prompts and LLM workflows with User Interfaces and take more of a holistic User Experience perspective. Tactic Generate has done an incredible job of that, please take a look from the link below! I had such a fun conversation catching up with Charles (Charles was our 2nd Weaviate Podcast guest!), I hope you enjoy the podcast!