Instructor

Optimizing LLMs in Insurance with DSPy: Jeronim Morina

Feb 16, 2025 · 19:29

Jeronim Morina from AXA Germany argues that AI engineers must stop relying on manual prompt tuning and instead adopt DSPy's automated optimization combined with first-principles thinking. He explains how his team built a customer-facing insurance chatbot by first crafting clear problem definitions, annotating evaluation data, and avoiding data leakage. After initial struggles with fragile prompts and overly complex error-handling code, they modularized their system into DSPy modules and created custom metrics for German text. Morina emphasizes that tools like DSPy require a steep learning curve and a solid baseline of hand-written prompts and basic evaluations before optimization. The episode calls for engineers to focus on real-world impact, measure everything, and break down problems into discrete, optimizable steps.

Architecting and Testing Controllable Agents: Lance Martin

Oct 11, 2024 · 2:21:54

Lance Martin presents LangGraph, a graph-based framework for building controllable agents that trade some open-ended flexibility for significantly higher reliability compared to classic React agents, achieving 100% consistent tool-calling trajectories even with an 8B local model. He demonstrates self-corrective RAG patterns like Corrective RAG, SelfRAG, and Adaptive RAG, where the agent grades retrieved documents, checks for hallucinations, and routes to web search when needed. Martin also covers three testing loops: in-app error handling with LangGraph, pre-production evaluation using LangSmith to compare agent answers and tool trajectories against ground-truth datasets, and production monitoring with online evaluators that flag retrieval quality, answer relevance, and hallucinations without reference answers. He shares results from a five-question evaluation showing LangGraph agents achieve 80% answer accuracy and 100% tool-trajectory correctness with Fire Function V2, while React agents with GPT-4o degrade in tool reliability. The talk addresses practical concerns like handling many tools (suggesting RAG for tool selection), multi-turn conversations, and the importance of…

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.

AI Engineering 201: The Rest of the Owl

Nov 8, 2023 · 56:57

Charles Frye, instructor of the Full Stack LLM Bootcamp, presents essential patterns for building language user interfaces (LUIs), arguing that while RAG chatbots are the 'to-do list app' of AI engineering, structured outputs via function calling (e.g., OpenAI's JSON schema, Instructor library) improve robustness, and agents with memory (like generative agents or Voyager in Minecraft) represent the true AI frontier. He emphasizes the need for hybrid search combining vector and keyword retrieval (citing Vespa, Postgres, Redis), and warns that monitoring and evaluation are the hardest engineering challenges: monitoring user behavior, latency quantiles (especially 99th percentile), and costs must be paired with observability tools like Honeycomb or Gantry, while evaluation often requires iterated decomposition or using LLMs as evaluators (GPT-4 as 90th percentile crowd worker). The episode concludes that shipping to learn — starting with production data to generate tests — is the dominant engineering mindset, and that the field is still filling in the gaps between inference and full product value.

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.