Jason Liu

How to look at your data — Jeff Huber (Chroma) + Jason Liu (567)

Aug 6, 2025 · 19:23

Jeff Huber (Chroma CEO) and Jason Liu argue that AI practitioners should look at their data—both inputs and outputs—with fast evals and conversation analysis to systematically improve retrieval and product decisions. Huber advocates for fast evals using golden datasets of query-document pairs over public benchmarks or expensive LLM judges, showing how synthetic queries aligned to real user behavior can empirically compare embedding models; in a Weights & Biases chatbot case study, Voyage 3 large outperformed text-embedding-3-small and others. Jason Liu focuses on outputs: extract structured metadata from conversations, cluster them to find segments, then compare KPIs like performance across clusters to prioritize what to fix, build, or ignore—for example, if 40% of conversations involve data visualization and the agent performs poorly, invest in better plotting tools. This population-level analysis enables impact-weighted decisions, turning vague metrics like 0.5 factuality into actionable insights by segment. The episode emphasizes that retrieval improvements are foundational, and once you have users, looking at conversation structure drives a data-driven product roadmap.

Pydantic is STILL all you need: Jason Liu

Sep 6, 2024 · 15:21

Jason Liu returns to the AI Engineer World's Fair to argue that Pydantic (and his library Instructor) is still all you need for structured output with LLMs. He shows that the core API—response_model, streaming with iterables, and partials for real-time validation—remains unchanged, now supporting Ollama, LlamaCPP, Anthropic, Gemini, and more. Liu demonstrates validators that enforce rules like uppercasing names or verifying receipt totals, reducing errors with automatic retries. He applies structured output to RAG: using a Search model with optional date ranges and source selection, and a Response model with follow-up questions and validated URLs. For extraction, he creates classifiers using Literal types, meeting summaries with action items, and even tables as Pandas DataFrames via custom type hints. Liu’s key takeaway is that one retry often suffices, and as models get faster and smarter, structured output makes LLMs compatible with classical programming—turning generative AI into generating data structures defined by the developer.

Lessons From A Year Building With LLMs

Jul 19, 2024 · 35:21

The six authors of the O'Reilly article "Lessons From A Year Building With LLMs" — Bryan Bischof, Jason Liu, Hamel Husain, Eugene Yan, Shreya Shankar, and Swix — argue that the model itself is not a moat and that success comes from continuous improvement centered on evals and data. They stress that AI engineers should treat models as SaaS, quickly swapping for better ones, and focus on product and user interactions. The talk warns against toxic practices like prematurely hiring ML engineers without data or blindly adopting tools, advocating instead for deliberate eval practice and data literacy. On tactics, they compare LLM-as-judge (quick to prototype) vs fine-tuned evaluators (more precise and faster), and emphasize looking at real user data regularly with automated guardrails. Ultimately, they conclude that going from demo to production requires sustained investment in infrastructure and evaluation, echoing MLOps lessons from a decade ago.

Pydantic is all you need: Jason Liu

Nov 1, 2023 · 17:55

Jason Liu argues that Pydantic is the key to building reliable LLM applications by using structured prompting with OpenAI function calling. He introduces Instructor, a library that patches OpenAI's API to return Pydantic objects instead of raw JSON, ensuring type safety and validation. Liu demonstrates how Pydantic's 70 million downloads make it a trusted tool for defining data models with type hints, field validators, and even LLM-powered validators that catch errors like "don't say mean things" and retry via max retries. He shows concrete examples: decomposing user queries into structured search objects for RAG, generating a query plan DAG with parallel dependencies, extracting knowledge graphs for visualization, and verifying facts by requiring substring quotes from source text. Liu emphasizes that structuring prompts as code moves development from string manipulation to domain modeling, enabling cleaner, more maintainable systems that integrate easily with existing software.