Weaviate Podcast

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142

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Aug 2026

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Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.

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August 26, 20261 hr 4 min

Recursive Language Models with Alex Zhang - Weaviate Podcast #142!

Alex Zhang, a PhD student at MIT, joins the Weaviate Podcast to discuss Recursive Language Models (RLMs), a new abstraction for designing agent harnesses. Instead of the standard ReAct-style loop that stuffs every tool observation into an ever-growing prompt, an RLM treats the prompt as a variable in a program. The model writes code that manipulates its own context and spawns recursive LLM calls over pieces of it. The published headline was long-context performance, but the deeper intention, inspired by how DSPy programmers decompose tasks, is letting the model do that decomposition itself, relieving context pressure so each call only sees a small, local, in-distribution problem. Stacking tool outputs into one giant sequence is terribly out of distribution for how models were trained. Frontier labs spend enormous resources making those long trajectories in-distribution, but RLMs aim to get more from the model without that.From there the conversation turns to PrimeAgent, Prime Intellect's production harness centered on an RLM, where the only tool is a persistent IPython REPL: search, compaction, sub-agents, and skills are all callable inside code, yielding strong results on ARC-AGI-3 and long-horizon benchmarks with big token savings. Alex shares why they moved from prompt tuning to RL on the weights of Qwen3, and why Claude Code's dynamic workflows are RLM-like but constrained by legacy tool-calling defaults.Alex then previews an unreleased library for speculative programmatic tool calling. Borrowing from speculative decoding and CPU speculative execution, a shadow executor queues up sub-agent calls while the main model is still generating code, delivering roughly 2x speedups and better GPU utilization for locally served agents. The discussion moves into running RLMs in the cloud with sandbox providers like Modal and Daytona, security concerns around code execution and prompt injection, and why serving engines should be redesigned around agents. It closes with RLMs and search, combining ColBERT-style retrievers as callable tools for problems like agent log analysis, and a theory that everything "long" is naturally decomposable. The podcast concludes with exciting ideas about where AI goes next from principled harness design to whether the transformer itself gets replaced with a newer neural architecture design.

August 17, 202656 min

Drowning in Documents with Mathew Jacob - Weaviate Podcast #141!

Mathew Jacob, lead author of "Drowning in Documents: Consequences of Scaling Reranker Inference" and now a PhD student in ML systems at the University of Washington, joins the Weaviate Podcast to unpack one of the most surprising results in modern search: cross-encoder rerankers get worse as you give them more documents. The paper began during his Databricks internship, where scaling reranking past roughly 100 documents sent recall@10 plummeting, a result so counterintuitive he assumed it was a bug.The conversation digs into why this happens, reframing rerankers through the lens of boosting, rather than being strictly stronger than first-stage retrievers. Cross-encoders are very good at correcting retriever errors within the distribution they were trained on. Full-scoring experiments over 10,000 randomly sampled documents drive the point home, with BM25 beating state-of-the-art cross-encoders. From there, the discussion moves into phantom hits, cases where wildly irrelevant documents scored highly. For example, a dishwasher document surfacing for a query about disease in Gabonese children. We also discuss whether ensembling rerankers can patch these false positives.The second half explores what comes next for reranking: prompt-based listwise reranking with sliding windows, which proved far more robust than pointwise scoring; RankZephyr-style fine-tuning versus encoding learning signal in prompts with GEPA and DSPy, reasoning rerankers like Rank1 and their latency trade-offs, hard negative mining behind ZeroEntropy's zELO, and pairwise and setwise designs that sit between cross-encoders and full listwise ranking. Adaptive retrieval comes into focus through Natural Language Query to Configuration for Retrieval Agents, predicting per query whether to run simple retrieval, multi-hop, or full agentic search to push the cost-quality frontier.The conversation lands on TraceLab, from Mathew's lab at UW: 40,000 real traces harvested from Claude Code and Codex usage, revealing how coding agents actually behave, prefix cache patterns, long-tailed tool calls, and how understanding these workloads unlocks the next generation of serving optimizations.

July 27, 202655 min

Founding Weaviate with Bob van Luijt and Etienne Dilocker - Weaviate Podcast #140!

Weaviate co-founders Bob van Luijt and Etienne Dilocker return to the Weaviate Podcast to celebrate seven years of building the company, answering questions submitted by the community. The conversation opens with what excites them most in AI right now: Etienne on agentic coding and the "Moore's law" of how long models can sustain autonomous loops, and Bob on world models, new architectures that could slash training energy costs, open source frontier models, and inference on new chips.From there, the discussion dives into taste and the "AI slopification" problem, why AI-generated emails, websites, and decks all look the same, how three job candidates submitted nearly identical AI-built presentations in one week, and why Weaviate runs a dedicated "slop pass" skill over every pull request to strip out phrases like "the smoking gun" and "load-bearing invariant." The human touch, they argue, is now the easiest way to stand out.The Co-Founders then retell their origin story: meeting at a European enterprise company, rewriting a NodeJS prototype in Go, betting on NLP before anyone called it AI, adopting HNSW when it was still a niche paper, and raising a $1.2M seed round from Zeta during COVID. When ChatGPT and the RAG paper hit, Weaviate had a fully working product ready for the wave.Looking forward, Bob breaks down the commoditization playbook that hits every new database category, the same skepticism MongoDB faced, and shares that the number one reason new customers cite for choosing Weaviate is that an LLM recommended it. Etienne makes the case that vector databases are evolving into context engines: context rot is real, stuffing everything into a long context window is inefficient, and retrieval, hybrid search, and structured data all serve one goal, the best possible context. The conversation lands on memory for AI agents, where the hard problem isn't what's worth remembering, but what's worth recalling.

June 1, 20261 hr 0 min

Knowledge Engineering with Bradley Allen - Weaviate Podcast #139!

Dr. Bradley Allen brings five decades of AI history into a deep conversation on knowledge engineering, neurosymbolic AI, and the future of enterprise intelligence. The discussion begins with the boom-and-bust cycle of rule-based expert systems, AI winters, and why today’s large language model wave may be different. The conversation then turns to how knowledge is organized in practice, from personal piles of papers searched on demand to formal knowledge graphs built with classes, relations, ontologies, A boxes, T boxes, description logic, and subsumption-based reasoning. Allen explains why semantic web and biomedical ontology successes still leave unresolved questions about cost, maintenance, and whether LLMs can dynamically structure information in ways that preserve meaning. That leads into natural language concept definitions, LLM-based classifiers, rationales, probabilistic reasoning, and the challenge of updating classes as new edge cases emerge.From there, the focus widens to vector databases, semantic search, RAG, topic modeling, distributional semantics, and the ongoing revision required for systems that can never be “once and done.” Allen connects modern LLM behavior to the long history of formal languages, from Frege, Russell, Wittgenstein, Turing, and Gödel to theorem proving, soundness, completeness, paraconsistency, paracompleteness, and the pragmatic tradition of meaning through use. The closing stretch explores world models, reinforcement learning, tool-using agents, enterprise knowledge workflows, role-based access control, governance, normativity, and alignment, ending on the need to build accountable AI systems that channel powerful technology toward responsible outcomes.

May 18, 202641 min

Booking.com and Weaviate with Başak Eskili - Weaviate Podcast #138!

Başak Eskili joins the Weaviate Podcast to explore how one of the world’s largest travel platforms adopted vector search, retrieval-augmented generation, and agentic AI at production scale. The conversation begins with Booking.com’s shift from keyword matching to semantic retrieval as internal teams needed embeddings, similarity search, and eventually GenAI RAG workflows. Başak explains why OpenSearch was a practical first step on AWS, how adoption grew across teams, and why hundreds of millions of embeddings, strict latency requirements, complex filtering, and rising concurrency pushed the platform toward Weaviate.The discussion then moves into Booking.com’s partner-to-guest messaging agent, a production GenAI system that helps accommodation partners answer guest questions about check-in, parking, special requests, and reservation details. Başak breaks down the tool-calling architecture, where Weaviate retrieves relevant response templates while GraphQL APIs fetch property and booking context. The agent can suggest templates, craft grounded replies, or decline to answer and leave the conversation to a human, highlighting the practical role of human-in-the-loop design. Evaluation spans offline datasets, LLM-as-a-judge scoring, A/B testing, and live partner feedback.From there, Başak describes the platform engineering behind AI at Booking.com: a central MCP server for internal APIs and external tools, a GenAI gateway for model access, PII reduction, guardrails, prompt injection detection, logging, traceability, and cost tracking across large-scale LLM usage. She also details Booking.com’s evaluation process of Weaviate, including 100 million embeddings, filtered vector search, multi-threaded concurrency testing, reads during writes, and cost-efficient infrastructure provisioning.The episode closes with Başak’s path from computer science and NLP to MLOps and AI platforms, then looks ahead to practical AI, personalized travel agents, and memory systems that capture user preferences, session context, semantic memory, and long-term personalization for future agentic travel experiences.

May 5, 20261 hr 1 min

Search Agents with Nandan Thakur - Weaviate Podcast #137!

Dr. Nandan Thakur returns to the Weaviate Podcast fresh off defending his dissertation to discuss the evolution from neural retrieval to agentic search and his new work on Orbit, a synthetic training data pipeline for search agents. The conversation opens with reflections on his PhD journey, tracing the field's shift from ColBERT-style models and sparse retrievers through RAG and into today's agentic search paradigm where LLMs iteratively search, reason, and refine.The discussion dives deep into how Orbit generates multi-hop, riddle-style training queries using DeepSeek's API on a personal laptop over four to six months, making high-quality search agent training data accessible without massive compute budgets. Thakur draws a sharp distinction between deep research (broad, multi-tool report generation) and search agents (focused on search and browse tools to answer specific questions), then connects Orbit's multi-hop queries to BrowseComp's filter-style riddles where each clue narrows the answer space like a funnel. The conversation explores the design of deep research harnesses, chunking strategies, Anthropic's contextual retrieval for entity disambiguation, context compaction to manage bloated agent contexts, and memory services like Weaviate's Engram for compressing search results between reasoning rounds.From there, the episode tackles sequential versus parallel search trajectories, the pass@K approach to rollouts in GRPO training, and whether isolated trajectories should share progress through message passing. Thakur makes a compelling case for training search agents to produce keyword-focused queries optimized for BM25 versus semantic queries for dense retrieval: the idea that one query does not fit all search engines. The conversation closes on future directions: efficiency-focused Pareto frontiers for search agents, long-form report generation evaluation through TREC RAG, and the coming wave of multilingual and multimodal search benchmarks.

April 27, 20261 hr 3 min

AgentIR with Zijian Chen and Xueguang Ma - Weaviate Podcast #136!

Zijian Chen and Xueguang Ma from the University of Waterloo join the Weaviate Podcast to discuss AgentIR and why retrieval systems need to be redesigned from the ground up for AI agents. The conversation opens with a striking reframe: agents have become the primary consumers of search, inserting themselves as middleware between humans and information. Humans used to query search engines directly, now they delegate to ChatGPT, which searches on their behalf. This means retrieval algorithms are no longer optimized for their actual users.The discussion distinguishes reasoning-intensive retrieval from reasoning-aware retrieval. Reasoning-intensive tasks like BRIGHT involve single-hop queries where the connection between query and document is obscure but still one step. Agent IR tackles a fundamentally different problem, extremely multi-hop queries from benchmarks like BrowseComp-Plus, where each hop strictly depends on the previous one. The key insight behind AgentIR is that agents reveal their entire reasoning process in their reasoning traces, unlike humans who never write out their thought process. Existing retrievers discard this rich signal entirely. AgentIR jointly embeds the query and reasoning trace, training a retriever from scratch to exploit this agent-specific context.From there, the conversation covers BrowseComp-Plus, which extends OpenAI's BrowseComp with a fixed corpus to enable disentangled evaluation of agents and retrievers separately, something impossible when both the web and the search provider are black boxes. Building the corpus required over 400 hours of human annotation to ensure every hop in every reasoning chain had its supporting documents present. The discussion then moves into agent context management, contrasting compaction approaches with just-in-time memory retrieval from paged memory, referencing InfoFlow and the AgentFold paper. Xueguang shares a provocative take that neither single-vector nor multi-vector representations are optimal, arguing the field needs embeddings at the right granularity based on information density. The episode closes with Steven introducing AICI, Agent-Computer Interaction, as the successor to HCI, and Xueguang framing the open question of scaling search along two dimensions: deeper (more turns) versus wider (more parallel queries).

April 6, 202657 min

Data Agents with Shreya Shankar - Weaviate Podcast #135!

Shreya Shankar from UC Berkeley joins the Weaviate Podcast to discuss data agents, the Data Agent Benchmark, and DocETL. The conversation opens with defining what a data agent actually is, not just text-to-SQL over a single table, but an AI system that can reason across dozens of heterogeneous databases, flat files, and knowledge repositories to answer complex organizational questions. Shreya explains why this multi-database reality makes existing benchmarks insufficient, motivating the Data Agent Benchmark where the best-performing agent achieves only 34–37% pass@1 accuracy. From there, the discussion dives into where agents fail. They don't explore data properly, they generate broken regex patterns, they struggle with different SQL dialects, and they give up when datasets get large. Interestingly, agents tend to pull data into Pandas rather than use database operators directly, likely because LLMs are more fluent in Python than in the nuances of each SQL dialect. The conversation moves into semantic operators, natural language variants of relational algebra, filter, map, join, aggregation, where predicates like "Is this a sports article?" replace handwritten regex, with implementations ranging from per-row LLM calls to synthesized code. Shreya then presents DocETL, a declarative system for processing unstructured data that uses LLM agents to propose query rewrite strategies like chunking, splitting, and map-then-reduce decompositions, optimizing for both accuracy and cost on long documents. This leads into a broader discussion of declarative versus imperative agent design, the tradeoff between letting agents write arbitrary Python and constraining them within frameworks that handle optimization and caching. The conversation also explores tribal knowledge, structuring learned facts about data quality into retrievable tables so agents can reuse discoveries across queries, and connects to recent work on using LLMs to discover new database query rewrite rules. The episode closes with a reflection on how classical database principles like query optimization and cardinality estimation are finding new life in the age of LLM-powered data systems. 0:05 What are Data Agents? 2:10 Multi-Database Systems 9:44 Semantic Operators 13:18 Querying Databases with Python 17:05 DocETL 24:34 Advanced Text-to-SQL 29:30 Claude Code and Databases 34:34 Self-Driving Databases 42:00 Agent Memory for Querying Databases 53:48 Exciting Directions for AI

March 23, 20261 hr 21 min

Multi-Vector Search with Amélie Chatelain and Antoine Chaffin - Weaviate Podcast #134!

Amélie Chatelain and Antoine Chaffin from LightOn are leading the way in the next generation of search powered by Multi-Vector representations and Late Interaction. The podcast begins with what motivates them to work on Multi-Vector Search, continuing to discuss particular details such as the combination between lexical and semantic search, as well as bi-encoder speed with cross encoder accuracy. This discussion continues to present insights about training multi-vector models and how they differ from their single-vector predecessors. The conversation continues into particular successes of Late Interaction such as code, reasoning-intensive, and multimodal retrieval. Agents are great at searching with grep, but they are even better with ColGrep! Reasoning-Intensive Retrieval is a step change in how we think about search systems, beautifully enabled by both Late Interaction models and Agentic Search. Further, Multimodal Search, such as matching text with videos, is seeing massive benefits from Multi-Vector representations. The podcast continues to dive into the cost of MaxSim and how efficient methods such as MUVERA and PLAID can help. The podcast concludes with a presentation of their recent work on ColBERT-Zero, pre-training with Late Interaction instead of Single-Vector Dense Embedding models. LightOn are also the developers of PyLate, the world's leading open-source library for training these kinds of models.Chapters0:00 An Introduction to Multi-Vector Search6:00 Multi- vs. Single-Vector8:55 Comparison with Cross Encoders15:55 ColGrep for Coding Agents30:34 Reasoning-Intensive Retrieval42:02 Multimodal Multi-Vector48:34 The Cost of Multi-Vector53:26 MUVERA and PLAID1:06:18 ColBERT-Zero and PyLate1:08:35 ColBERT-Zero and PyLate

March 1, 202653 min

AI-Powered Search with Doug Turnbull and Trey Grainger [#133]

Doug Turnbull and Trey Grainger join the Weaviate Podcast to discuss all things AI-Powered Search! The conversation kicks off with designing search experiences, not all search queries are the same! Sometimes the user knows exactly what they want (a product ID, a specific file), other times they're exploring a broad category, and other times they need to compare and contrast options. AI is now making it possible to dynamically construct UIs around search results, moving toward what Trey describes as a "Minority Report"-style future where visualizations adapt on the fly to the query and the data.From there, the discussion dives into query understanding and domain modeling. Doug and Trey break down how LLMs can classify queries against existing taxonomies (like NAICS codes or Google's product taxonomy), while Trey explains a multi-tier RAG approach, using the index itself as grounding for query interpretation before executing the final retrieval. The conversation moves into agentic search, exploring whether iterative LLM-driven search loops reduce the need for ever-better embedding models, or whether simple tools like BM25 and grep are sufficient when paired with strong reasoning.Trey introduces wormhole vectors, a technique for traversing between sparse (lexical) and dense (semantic) vector spaces by treating query results as document sets with shared meaning, enabling exploration across vector spaces rather than treating them as orthogonal. The discussion also covers reflected intelligence, the idea of making search systems self-learning by mining user behavioral signals (clicks, purchases, skipped results) to continuously improve relevance through techniques like signals boosting, collaborative filtering, and learning to rank.The episode wraps with a conversation about how coding agents are changing the way Doug and Trey work, and Trey's philosophy of designing intentional agentic workflows with atomic agents rather than just handing an LLM a bag of tools.AI Powered Search (Discount Code = "weaviate")https://aipoweredsearch.com/live-course?promoCode=weaviate