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Dive into the heart of creativity and business with the Accidental CEO Podcast, your go-to audio hangout for turning passion into a thriving business. Hosted by Nata Salvatori, a seasoned wedding photographer and savvy business educator, this podcast peels back the curtain on the accidental journey from creative professional to CEO. Whether you stumbled upon the role of entrepreneur or have always dreamt of leading your own venture, this podcast is crafted for you. Each episode is a blend of inspiring stories, practical strategies, and real-world advice to navigate the complexities of running a creative business. From building a brand that resonates with your ideal clients to mastering the art of sustainable growth, Nata and a roster of guest experts offer insights into making the leap from creative to CEO not just possible, but profitable and fulfilling. The Accidental CEO Podcast is more than just a podcast; it's a community of creative minds eager to transform their accidental titles into intentional successes. So, if you're passionate about what you do but unsure how to fall in love with the business side of things, let Nata guide you. Subscribe to the Accidental CEO Podcast and join a vibrant community of creative entrepreneurs ready to make their mark on the business world. Together, let's turn your passion project into your success story. Connect with Nata: accidentalceopod.com instagram.com/accidentalceopod
Optimizing Retrieval Agents with Shirley Wu - Weaviate Podcast #115!
Manage episode 467443061 series 3524543
Content provided by Weaviate. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Weaviate or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Hey everyone! Thank you so much for watching the 115th episode of the Weaviate Podcast featuring Shirley Wu from Stanford University!
We explore the innovative Avatar Optimizer—a novel framework that leverages contrastive reasoning to refine LLM agent prompts for optimal tool usage. Shirley explains how this self-improving system evolves through iterative feedback by contrasting positive and negative examples, enabling agents to handle complex tasks more effectively.
We also dive into the STaRK Benchmark, a comprehensive testbed designed to evaluate retrieval systems on semi-structured knowledge bases. The discussion highlights the challenges of unifying textual and relational retrieval, exploring concepts such as multi-vector embeddings, relational graphs, and dynamic data modeling. Learn how these approaches help overcome information loss, enhance precision, and enable scalable, context-aware retrieval in diverse domains—from product recommendations to precision medicine.
Whether you’re interested in advanced prompt optimization, multi-agent system design, or the future of human-centered language models, this episode offers a wealth of insights and a forward-looking perspective on integrating sophisticated AI techniques into real-world applications.
127 episodes
Share
Manage episode 467443061 series 3524543
Content provided by Weaviate. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Weaviate or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Hey everyone! Thank you so much for watching the 115th episode of the Weaviate Podcast featuring Shirley Wu from Stanford University!
We explore the innovative Avatar Optimizer—a novel framework that leverages contrastive reasoning to refine LLM agent prompts for optimal tool usage. Shirley explains how this self-improving system evolves through iterative feedback by contrasting positive and negative examples, enabling agents to handle complex tasks more effectively.
We also dive into the STaRK Benchmark, a comprehensive testbed designed to evaluate retrieval systems on semi-structured knowledge bases. The discussion highlights the challenges of unifying textual and relational retrieval, exploring concepts such as multi-vector embeddings, relational graphs, and dynamic data modeling. Learn how these approaches help overcome information loss, enhance precision, and enable scalable, context-aware retrieval in diverse domains—from product recommendations to precision medicine.
Whether you’re interested in advanced prompt optimization, multi-agent system design, or the future of human-centered language models, this episode offers a wealth of insights and a forward-looking perspective on integrating sophisticated AI techniques into real-world applications.
127 episodes
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Weaviate Podcast
1 Agentic Topic Modeling with Maarten Grootendorst - Weaviate Podcast #126! 1:05:18
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Play Later Play Later Lists Like LikedMaarten Grootendorst is a psychologist turned AI engineer who has created BERTopic and authored "Hands-On Large Language Models" with Jay Alammar. The rise of LLMs and Agents are transforming many areas of software! This podcast dives deep into their impact on Topic Modeling! Maarten designed BERTopic from the start with modularity in mind -- letting you ablate embedding models, dimensionality reduction, clustering algorithms, and more. This early insight to prioritize modularity makes BERTopic incredibly well structured to become more "Agentic". An "Agentic" Topic Modeling algorithm can use LLMs to generate topics or topic descriptions, as well as contrast them with other topics. It can decide which topics to subdivide, and it can integrate human feedback and evaluate topics in novel ways... I hope you find the podcast interesting!…
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Weaviate Podcast
1 Sufficient Context with Hailey Joren - Weaviate Podcast #125! 50:53
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Play Later Play Later Lists Like LikedHailey Joren is a Ph.D. student at UCSD! Hailey and collaborators at Duke University and Google have recently published Sufficient Context: A New Lens on Retrieval Augmented Generation Systems in ICLR 2025! There are so many interesting nuggets to this work! Firstly, it really helped me understand the difference between *relevant* search results and sufficient context for answering the question. Armed with this lens of looking at retrieved context, Hailey and collaborators make all sorts of interesting observations about the current state of Hallucination. RAG unfortunately makes the models far less likely to hallucinate, and the existing RAG benchmarks unfortunately do not emphasize retrieval adaptation well enough -- indicated by LLMs outputting correct answers despite insufficient context 35-62% of the time! However, reason for optimism! Hailey and team develop an autorater that can detect insufficient context 93% of the time! There are all sorts of interesting ideas around this paper! I really hope you find the podcast useful!…
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Weaviate Podcast
1 RAG Benchmarks with Nandan Thakur - Weaviate Podcast #124! 1:04:46
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Play Later Play Later Lists Like LikedNandan Thakur is a Ph.D. student at the University of Waterloo! Nandan has worked on many of the most impactful works in Retrieval-Augmented Generation (RAG) and Information Retrieval. His work ranges from benchmarks such as BEIR, MIRACLE, TREC, and FreshStack, to improving the training of embedding models and re-rankings, and more!…
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Weaviate Podcast
1 MUVERA with Rajesh Jayaram and Roberto Esposito - Weaviate Podcast #123! 1:13:06
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Play Later Play Later Lists Like LikedMulti-vector retrieval offers richer, more nuanced search, but often comes with a significant cost in storage and computational overhead. How can we harness the power of multi-vector representations without breaking the bank? Rajesh Jayaram, the first author of the groundbreaking MUVERA algorithm from Google, and Roberto Esposito from Weaviate, who spearheaded its implementation, reveal how MUVERA tackles this critical challenge. Dive deep into MUVERA, a novel compression technique specifically designed for multi-vector retrieval. Rajesh and Roberto explain how it leverages contextualized token embeddings and innovative fixed dimensional encodings to dramatically reduce storage requirements while maintaining high retrieval accuracy. Discover the intricacies of quantization within MUVERA, the interpretability benefits of this approach, and how LSH clustering can play a role in topic modeling with these compressed representations. This conversation explores the core mechanics of efficient multi-vector retrieval, the challenges of benchmarking these advanced systems, and the evolving landscape of vector database schemas designed to handle such complex data. Rajesh and Roberto also share their insights on the future directions in artificial intelligence where efficient, high-dimensional data representation is paramount. Whether you're an AI researcher grappling with the scalability of vector search, an engineer building advanced retrieval systems, or fascinated by the cutting edge of information retrieval and AI frameworks, this episode delivers unparalleled insights directly from the source. You'll gain a fundamental understanding of MUVERA, practical considerations for its application in making multi-vector retrieval feasible, and a clear view of future directions in AI.…
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Weaviate Podcast
1 Patronus AI with Anand Kannappan - Weaviate Podcast #122! 1:01:06
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Play Later Play Later Lists Like LikedAI agents are getting more complex and harder to debug. How do you know what's happening when your agent makes 20+ function calls? What if you have a Multi-Agent System orchestrating several Agents? Anand Kannappan, co-founder of Patronus AI, reveals how their groundbreaking tool Percival transforms agent debugging and evaluation. Percival can instantly analyze complex agent traces, it pinpoints failures across 60 different modes, and it automatically suggests prompt fixes to improve performance. Anand unpacks several of these common failure modes. This includes the critical challenges of "context explosion" where agents process millions of tokens. He also explains domain adaptation for specific use cases, and the complex challenge of multi-agent orchestration. The paradigm of AI Evals is shifting from static evaluation to dynamic oversight! Also learn how Percival's memory architecture leverages both episodic and semantic knowledge with Weaviate!This conversation explores powerful concepts like process vs. outcome rewards and LLM-as-judge approaches. Anand shares his vision for "agentic supervision" where equally capable AI systems provide oversight for complex agent workflows. Whether you're building AI agents, evaluating LLM systems, or interested in how debugging autonomous systems will evolve, this episode delivers concrete techniques. You'll gain philosophical insights on evaluation and a roadmap for how evaluation must transform to keep pace with increasingly autonomous AI systems.…
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Weaviate Podcast
1 Haize Labs with Leonard Tang - Weaviate Podcast #121! 54:15
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Play Later Play Later Lists Like LikedHow do you ensure your AI systems actually do what you expect them to do? Leonard Tang takes us deep into the revolutionary world of AI evaluation with concrete techniques you can apply today. Learn how Haize Labs is transforming AI testing through "scaling judge-time compute" - stacking weaker models to effectively evaluate stronger ones. Leonard unpacks the game-changing Verdict library that outperforms frontier models by 10-20% while dramatically reducing costs. Discover practical insights on creating contrastive evaluation sets that extract maximum signal from human feedback, implementing debate-based judging systems, and building custom reward models that align with enterprise needs. The conversation reveals powerful nuggets like using randomized agent debates to achieve consensus and lightweight guardrail models that run alongside inference. Whether you're developing AI applications or simply fascinated by how we'll ensure increasingly powerful AI systems perform as expected, this episode delivers immediate value with techniques you can implement right away, philosophical perspectives on AI safety, and a glimpse into the future of evaluation that will fundamentally shape how AI evolves.…
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Weaviate Podcast
Ben walks us through Box's three-layer infrastructure puzzle: First, the mind-boggling base infrastructure (think millions of interactions per second and trillions of files). Second, their unique multi-tenant security challenge - unlike most SaaS platforms, Box users share content across company boundaries, making traditional tenant isolation impossible. And third, ensuring AI respects all these complex permissions while still delivering value. The podcast then dives further into how vector embeddings can balloon file sizes - a few hundred bytes of text can require 4-6KB of vector data storage! We also dig into why RAG remains essential despite growing context windows, and how Box is developing AI agents that transform painful enterprise processes like RFP responses.…
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Weaviate Podcast
1 Structured Outputs with Will Kurt and Cameron Pfiffer - Weaviate Podcast #119! 1:10:17
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Play Later Play Later Lists Like LikedHey everyone! Thanks so much for watching another episode of the Weaviate Podcast! Dive into the fascinating world of structured outputs with Will Kurt and Cameron Pfeiffer, the brilliant minds behind Outlines, the revolutionary open-source library from .txt.ai that's changing how we interact with LLMs. In this episode, we explore how constrained decoding enables predictable, reliable outputs from language models—unlocking everything from perfect JSON generation to guided reasoning processes.Will and Cameron share their journey to founding .txt.ai, explain the technical magic behind Outlines (hint: it involves finite state machines!), and debunk misconceptions around structured generation performance. You'll discover practical applications like knowledge graph construction, metadata extraction, and report generation that simply weren't possible before this technology.Whether you're building AI systems or curious about where the field is heading, you'll gain valuable insights on how structured outputs integrate with inference engines like vLLM, why multi-task inference outperforms single-task approaches, and how this technology enables scalable agent systems that could transform software architecture forever. Join us for this mind-expanding conversation about one of AI's most important but under appreciated innovations—and discover why the future might belong to systems that combine freedom with structure.…
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Weaviate Podcast
1 Synthetic Data with David Berenstein and Ben Burtenshaw - Weaviate Podcast #118! 1:02:01
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Play Later Play Later Lists Like LikedSynthetic Data: The Building Bocks of AI's Future! Hey everyone! I am SUPER EXCITED to publish the 118th episode of the Weaviate Podcast featuring David Berenstein and Ben Burtenshaw from HuggingFace! This podcast explores the intricacies of synthetic data generation, detailing methodologies such as data augmentation, distillation, and instruction refinement. The conversation delves into persona-driven synthetic data, highlighting applications like Persona Hub, and discusses algorithms to enhance diversity, complexity, and quality of generated data. Additionally, they cover integration with Hugging Face’s ecosystem, including Argilla for annotation, AutoTrain for fine-tuning, and advanced data exploration tools like the Data Studio and SQL console. The podcast also touches upon the potential for synthetic image data generation and the exciting future of AI education and accessibility.…
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Weaviate Podcast
1 Letta AI with Sarah Wooders - Weaviate Podcast #117! 57:34
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 117th episode of the Weaviate podcast! In this episode, we dive deep into the cutting edge of AI agent development with Sarah Wooders, co-founder and CTO of Letta AI. Emerging from Berkeley's Sky Computing Lab, Sarah and her team have pioneered a revolutionary approach to stateful agents - AI systems that genuinely remember both you and themselves across extended conversations. The conversation explores how the groundbreaking MemGPT project evolved into Letta's comprehensive Agent Development Environment (ADE), which empowers developers to build truly persistent AI experiences. Sarah shares powerful insights on context management, memory prioritization, and the critical role of databases in agent architecture. Whether you're building AI systems or simply curious about where conversational AI is heading, this episode illuminates how the future of agents depends not just on their reasoning capabilities, but on their ability to maintain coherent identity and memory over time.…
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Weaviate Podcast
1 Agent Experience with Matt Biilmann, Sebastian Witalec, and Charles Pierse - Weaviate Podcast #116! 52:09
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching another episode of the Weaviate Podcast! I am SUPER excited to welcome Matt Biilmann, Co-Founder and CEO of Netlify, as well as Sebastian Witalec and Charles Pierse from Weaviate to discuss Agent Experience! You have probably heard about how you can connect LLMs to external software tools. This supercharges the capabilities of AI systems and what they can do. So what does that mean for you as a software developer?This podcast explores different ideas around designing software user experiences for Agents as well as Humans. How do we write documentation for Agents differently than Humans? How do we design REST or gRPC APIs, or programming languages clients, for Agents differently than Humans? llms.txt, JSON tool definitions, agents to agent, breaking changes, … there were so many interesting topics explored in this podcast! I really hope you find it useful! As always more than happy to discuss these ideas further with you!…
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Weaviate Podcast
1 Optimizing Retrieval Agents with Shirley Wu - Weaviate Podcast #115! 1:00:20
1:00:20 Play Later Play Later Lists Like Liked1:00:20
Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 115th episode of the Weaviate Podcast featuring Shirley Wu from Stanford University! We explore the innovative Avatar Optimizer—a novel framework that leverages contrastive reasoning to refine LLM agent prompts for optimal tool usage. Shirley explains how this self-improving system evolves through iterative feedback by contrasting positive and negative examples, enabling agents to handle complex tasks more effectively. We also dive into the STaRK Benchmark, a comprehensive testbed designed to evaluate retrieval systems on semi-structured knowledge bases. The discussion highlights the challenges of unifying textual and relational retrieval, exploring concepts such as multi-vector embeddings, relational graphs, and dynamic data modeling. Learn how these approaches help overcome information loss, enhance precision, and enable scalable, context-aware retrieval in diverse domains—from product recommendations to precision medicine. Whether you’re interested in advanced prompt optimization, multi-agent system design, or the future of human-centered language models, this episode offers a wealth of insights and a forward-looking perspective on integrating sophisticated AI techniques into real-world applications.…
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Weaviate Podcast
1 Contextual AI with Amanpreet Singh - Weaviate Podcast #114! 57:56
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 114th episode of the Weaviate Podcast featuring Amanpreet Singh, Co-Founder and CTO of Contextual AI! Contextual AI is at the forefront of production-grade RAG agents! I learned so much from this conversation! We began by discussing the vision of RAG 2.0, jointly optimizing generative and retrieval models! This then lead us to discuss Agentic RAG and how the RAG 2.0 roadmap is evolving with emerging perspectives on tool use. Amanpreet continues to further motivate the importance of continual learning of the model and the prompt / few-shot examples -- discussing the limits of prompt engineering. Personally I have to admit I think I have been a bit too bullish on only tuning instructions / examples, Amanpreet made an excellent case for updating the weights of the models as well -- citing issues such as parametric knowledge conflicts, and later on discussing how Mechanistic Interpretability is used to audit models and their updates in enterprise settings. We then discussed Contextual AI's LMUnit for evaluating these systems. This then lead us into my favorite part of the podcast, a deep dive into RL algorithms for LLMs. I highly recommend checking out the links below to learn more about Contextual's innovations on APO and KTO! We then discuss the importance of domain specific data, Mechanistic Interpretability, return to another question on RAG 2.0, and conclude with Amanpreet's most exciting future directions for AI! I hope you enjoy the podcast!…
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Weaviate Podcast
1 Cartesia AI with Karan Goel - Weaviate Podcast #113! 53:45
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 113th episode of the Weaviate Podcast with Karan Goel from Cartesia AI! Cartesia AI is leading the AI world in text-to-speech models! As exciting as these new applications in speech generation are, Cartesia is also building around an incredibly exciting new neural network architecture that cuts across all of AI -- State Space Models. State Space Models (SSMs) present a new approach to modeling long sequences circumventing the quadratic attention bottlenecks of transformers. In the podcast, we discuss Karan's perspectives around end-to-end modeling, long context and Multimodal processing, building and deploying a new kind of model, and more! I hope you find the podcast interesting and useful! As always more than happy to discuss these ideas further with you! Thanks for listening!…
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Weaviate Podcast
1 Google Vertex AI RAG Engine with Lewis Liu and Bob van Luijt - Weaviate Podcast #112! 58:16
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 112th episode of the Weaviate Podcast! This is another super exciting one, diving into the release of the Vertex AI RAG Engine, its integration with Weaviate and thoughts on the future of connecting AI systems with knowledge sources! The podcast begins by reflecting on Bob's experience speaking at Google in 2016 on Knowledge Graphs! This transitions into discussing the evolution of knowledge representation perspectives and things like the semantic web, ontologies, search indexes, and data warehouses. This then leads to discussing how much knowledge is encoded in the prompts themselves and the resurrection of rule-based systems with LLMs! The podcast transitions back to topics around the modern consensus in RAG pipeline engineering. Lewis suggests that parsing in data ingestion is the biggest bottleneck and low hanging fruit to fix. Bob presents the re-indexing problem and how it is additionally complicated with embedding models! Discussing the state of knowledge representation systems inspired me to ask Bob further about his vision with Generative Feedback Loops and controlling databases with LLMs, How open ended will this be? We then discuss the role that Agentic Architectures and Compound AI Systems are having on the state of AI. What is the right way to connect prompts with other prompts, external tools, and agents? The podcast then concludes by discussing a really interesting emerging pattern in the deployment of RAG systems. Whereas the first generation of RAG systems typically were user facing, such as customer support chatbots, the next generation is more API-based. The launch of the Vertex AI RAG Engine quickly shows you how to use RAG Engine as a tool for a Gemini Agent!…
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Weaviate Podcast
1 Morningstar Intelligence Engine with Aravind Kesiraju - Weaviate Podcast #111! 53:25
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Play Later Play Later Lists Like LikedHey everyone! I am SUPER EXCITED to publish the 111th Weaviate Podcast with Aravind Kesiraju from Morningstar! Aravind is a Principal Software Engineer who has lead the development behind the Morningstar Intelligence Engine! There are so many interesting aspects to this, and if you are building Agentic systems that would benefit from a high-quality financial retrieval API, you should check this out right now! The podcast dives into all sorts of ingredients that went into building this system: from custom RAG data pipelines with content management system integrations and embedding task queues, to exploring new chunking strategies, tool marketplaces, ReAct Agents, Text-to-SQL, and all sorts of other things!…
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Weaviate Podcast
1 Arctic Embed with Luke Merrick, Puxuan Yu, and Charles Pierse - Weaviate Podcast #110! 1:33:39
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 110th episode of the Weaviate Podcast! Today we are diving into Snowflake’s Arctic Embedding model series and their newly released Arctic Embed 2.0 open-source model, additionally supporting multilingual text embeddings. The podcast covers the origin of Arctic Embed, Pre-training embedding models, Matryoshka Representation Learning (MRL), Fine-tuning embedding models, Synthetic Query Generation, Hard Negative Mining, and Single-Vector Embeddings Models in the cohort of Multi-Vector ColBERT, SPLADE, and Re-rankers.…
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Weaviate Podcast
1 Agentic RAG with Erika Cardenas - Weaviate Podcast #109! 34:08
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 109th episode of the Weaviate Podcast with Erika Cardenas! Erika, in collaboration with Leonie Monigatti, have recently published "What is Agentic RAG". This blog post that was even covered in VentureBeat with additional quotes from Weaviate Co-Founder and CEO Bob van Luijt! This podcast continues the discussion on all things Agentic RAG, covering the basics of Agents, how Agentic RAG changes the game compared to Vanilla RAG systems, Multi-Agent Systems and CrewAI / OpenAI Swarm, Letta, DSPy, and many more! The podcast also anchors by discussing Agentic Generative Feedback Loops and how we are using Agents to improve the quality and expand the capabilities of Generative Feedback Loops!…
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Weaviate Podcast
1 Let Me Speak Freely? with Zhi Rui Tam - Weaviate Podcast #108! 40:04
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Play Later Play Later Lists Like LikedJSON mode has been one of the biggest enablers for working with Large Language Models! JSON mode is even expanding into Multimodal Foundation models! But how exactly is JSON mode achieved? There are generally 3 paths to JSON mode: (1) constrained generation (such as Outlines), (2) begging the model for a JSON response in the prompt, and (3) A two stage process of generate-then-format. I am BEYOND EXCITED to publish the 108th Weaviate Podcast with Zhi Rui Tam, the lead author of Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models! As the title of the paper suggests, although constrained generation is awesome because of its reliability, we may be sacrificing the performance of the LLM by producing our JSON with this method. The podcast dives into how these experiments identify this and all sorts of details about the potential and implementation details of Structured Outputs. I particularly love the conversation topic of incredible Complex Structured Outputs, such as generating 10 values in a single inference. I hope you enjoy the podcast! As always please reach out if you would like to discuss any of these ideas further!…
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Weaviate Podcast
1 SWE-bench with John Yang and Carlos E. Jimenez - Weaviate Podcast #107! 58:23
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 107th episode of the Weaviate Podcast! This one dives into SWE-bench, SWE-agent, and most recently SWE-bench Multimodal with John Yang from Stanford University and Carlos E. Jimenez from Princeton University! One of the most impactful applications of AI we have seen so far is in programming and software engineering! John, Carlos, and team are at the cutting-edge of developing and benchmarking these systems! I learned so much from the conversation and I really hope you find it interesting and useful as well!…
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Weaviate Podcast
1 AI in Education with Rose E. Wang - Weaviate Podcast #106! 51:15
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Play Later Play Later Lists Like LikedHey everyone! I am SUPER excited to publish the 106th episode of the Weaviate Podcast featuring Rose E. Wang!! Rose is a Ph.D. student at Stanford University where she has lead incredible research at the cutting-edge of AI applications in Education. The podcast heavily discusses her recent work on Tutor CoPilot! Tutor CoPilot is one of the world's largest randomized control trials on the impact AI is having on education, testing 900 students and 1800 tutors in grades K-12. I think this is such an inspiring study and it is interesting to see the data coming in quantifying the impact AI is having on education. I was amazed by the depth of how Rose things about education and learning strategies and how well she integrates cutting-edge topics in AI! I hope you find the podcast interesting and useful!…
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Weaviate Podcast
1 Compound AI Systems with Philip Kiely - Weaviate Podcast #105! 56:50
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Play Later Play Later Lists Like LikedHey everyone! Thank you so much for tuning into the 105th episode of the Weaviate Podcast! This one features Philip Kiely diving into all sorts of apsects related to Compound AI Systems! We are now seeing far better results with AI models by breaking up tasks into multiple stages and inferences. Philip explains the work they are doing at Baseten to optimize and scale deployments of these emerging systems and all sorts of aspects about them from Structured Generation to their distinction with Agents! I hope you find it useful!…
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Weaviate Podcast
1 AI Agents That Matter with Sayash Kapoor and Benedikt Stroebl - Weaviate Podcast #104! 1:00:43
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Play Later Play Later Lists Like LikedAI Researchers have overfit to maximizing state-of-the-art accuracy at the expense of the cost to run these AI systems! We need to account for cost during optimization. Even if a chatbot can produce an amazing answer, it isn't that valuable if it costs, say $5 per response! I am beyond excited to present the 104th Weaviate Podcast with Sayash Kapoor and Benedikt Stroebl from Princeton Language and Intelligence! Sayash and Benedikt are co-first authors of "AI Agents That Matter"! This is one of my favorite papers I've studied recently which introduces Pareto Optimal optimization to DSPy and really tames the chaos of Agent benchmarking! This was such a fun conversation! I am beyond grateful to have met them both and to feature their research on the Weaviate Podcast! I hope you find it interesting and useful!…
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Weaviate Podcast
1 MIPRO and DSPy with Krista Opsahl-Ong! - Weaviate Podcast #103 1:00:36
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Play Later Play Later Lists Like LikedI am beyond excited to publish our interview with Krista Opsahl-Ong from Stanford University! Krista is the lead author of MIPRO, short for Multi-prompt Instruction Proposal Optimizer, and one of the leading developers and scientists behind DSPy! This was such a fun discussion beginning with the motivation of Automated Prompt Engineering, Multi-Layer Language Programs (also commonly referred to as Compound AI Systems), and their intersection. We then dove into the details of how MIPRO achieves this and miscellaneous topics in AI from Structured Outputs to Agents, DSPy for Code Generation, and more! I really hope you enjoy the podcast! As always, more than happy to answer any questions or discuss any ideas about the content in the podcast!…
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Weaviate Podcast
1 AI-Native Development with Guy Podjarny and Bob van Luijt - Weaviate Podcast #102! 52:36
52:36 Play Later Play Later Lists Like Liked52:36
Play Later Play Later Lists Like LikedAI is completely transforming how we build software! But how exactly? What does it mean for a software application to be AI-Native versus AI-Enabled? How many other aspects of software development and creativity are impacted by AI? I am super excited to publish our 102nd Weaviate Podcast with Guy Podjarny and Bob van Luijt on AI-Native Development! Guy Podjarny is a co-founder of Snyk, a remarkably successful Cybersecurity company. He is now back on the founder journey, diving into AI-Native Development with Tessl! Guy and Bob both have so much expertise in how software is developed and shipped to the world. There are so many interesting nuggets in this from defining AI-Native to Stateful AI, AI-assisted coding, subjectivity in software specification, personalized content, and much more! I hope you enjoy the podcast, this was a really fun and interesting one!…
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Weaviate Podcast
1 Scaling Pandas with Devin Petersohn - Weaviate Podcast #101! 47:49
47:49 Play Later Play Later Lists Like Liked47:49
Play Later Play Later Lists Like LikedHey everyone! Thank you so much for watching the 101st episode of the Weaviate Podcast with Devin Petersohn! Devin is the creator of Modin, one of the world's most advanced systems for scaling Pandas! Devin then went onto co-found Ponder, which was acquired by Snowflake in early 2023. This was one of my favorite podcasts of all time, I learned so much about the internals of Data Systems and I hope you do as well!…
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Weaviate Podcast
1 Generative UIs with Lucas Negritto and Bob van Luijt! 43:40
43:40 Play Later Play Later Lists Like Liked43:40
Play Later Play Later Lists Like LikedWhat is an AI-native application? This has been one of the questions we are most interested in answering at Weaviate! This podcast explores this question with Weaviate Co-founder Bob van Luijt and Lucas Negritto. Formerly at OpenAI, Lucas is now building Odapt, a remarkable example of such an application where we no longer use front-end code, rather rendering the UI entirely within the generative model!! There are many interesting topics covered such as of course, firstly how this works and how you build these systems, as well as native multimodality, subjective feedback, and more! I hope you find the podcast interesting and useful!…
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Weaviate Podcast
1 ACORN with Liana Patel and Abdel Rodriguez - Weaviate Podcast #99! 53:55
53:55 Play Later Play Later Lists Like Liked53:55
Play Later Play Later Lists Like LikedLiana Patel is a Ph.D. student at Stanford University who is the lead author of ACORN, a breakthrough in Approximate Nearest Neighbor Search with Filters! Also joining the podcast is Abdel Rodriguez, a Vector Index Researcher and Engineer at Weaviate. This podcast dives into all sorts of details behind ACORN. Starting with how Liana developed her interest in Approximate Nearest Neighbor Search algorithms and then transitioning into how ACORN differs from previous approaches, the Two-Hop Neighborhood Heuristic, Predicate Subgraphs, Experimental Details, and many more topics! Major thank you to Liana and Abdel for joining the podcast, this was such a fun conversation packed with insights about Proximity Graph algorithms for Vector Search with Filtering!…
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Weaviate Podcast
1 Window Search Tree with Josh Engels - Weaviate Podcast #98! 58:56
58:56 Play Later Play Later Lists Like Liked58:56
Play Later Play Later Lists Like LikedJosh Engels is a Ph.D. student at MIT who has published several works advancing the state of the art in Vector Search. Josh has recently developed the Window Search Tree, a new algorithm particularly targeted for improving Filtered Vector Search. Even more particularly than that, the WST algorithm targets Filtered Search with continuous-valued filters such as "price" or "date", also known as range filters. This is a huge application for Vector Databases and it was incredible getting to pick Josh's brain on how this works and the state of Approximate Nearest Neighbor Search!…
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Weaviate Podcast
1 The Future of Search with Nils Reimers and Erika Cardenas - Weaviate Podcast #97! 59:41
59:41 Play Later Play Later Lists Like Liked59:41
Play Later Play Later Lists Like LikedHey everyone! I am SUPER excited to publish our 97th Weaviate Podcast on the state of AI-powered Search technology featuring Nils Reimers and Erika Cardenas! Erika and I have been super excited about Cohere's latest works to advance RAG and Search and it was amazing getting to pick Nils' brain about all these topics! We began with the development of Compass! Nils explains the current problem with embeddings as a soup!! For example, imagine embedding this video description, the first part is about the launch of a podcast, whereas this part is about an embedding algorithm -- how do we form representations of multi-aspect chunks of text? We dove into all the details of this from the distinction of multi-aspect embeddings with LLM or "smart" chunkers, ColBERT, "Embed Small, Retrieve Big", and many other topics as well from Cross Encoder Re-rankers to Data Cleaning with Generative Feedback Loops, RAG Evaluation, Vector Quantization, and more! I really hope you enjoy the podcast! It was such an educational experience for Erika and I and we really hope you enjoy it as well!…
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