How to Build and Evaluate AI systems in the Age of LLMs - Hugo Bowne-Anderson

Oct 24, 2025

Hugo Bowne-Anderson, an independent AI consultant and educator, shares insights from his journey from academia to advising major companies like Netflix and Meta. He discusses how to build reliable AI systems, focusing on practical tips for prompt evaluation and dataset design. Hugo emphasizes the importance of structuring teams for successful AI adoption and offers strategies to avoid common pitfalls like prompt overfitting. Listeners will learn about debugging tools and the evolution of proactive AI agents that enhance productivity in everyday workflows.

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ADVICE

Design Prompts And Add An Evaluator Loop

Give prompts a clear role, objective, few-shot examples, and heuristics to improve outputs.
Build an evaluator-optimizer loop so one model scores outputs and another revises until they pass.

ADVICE

Save Prompts And Automate The Pipeline

Save and reuse prompts that perform well across representative examples instead of redoing prompts for each transcript.
Automate the pipeline (GitHub Actions, etc.) to process transcripts and keep consistent quality at scale.

INSIGHT

Make Eval Sets Representative And Practical

Use a representative, not huge, evaluation set and cheaper automated checks where possible to control costs.
Inspect data in spreadsheets to uncover failure modes and guide how large your test set must be.

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In this talk, Hugo Bowne-Anderson, an independent data and AI consultant, educator, and host of the podcasts Vanishing Gradients and High Signal, shares his journey from academic research and curriculum design at DataCamp to advising teams at Netflix, Meta, and the US Air Force. Together, we explore how to build reliable, production-ready AI systems—from prompt evaluation and dataset design to embedding agents into everyday workflows.

You’ll learn about:

How to structure teams and incentives for successful AI adoption
Practical prompting techniques for accurate timestamp and data generation
Building and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoring
Tools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)
The evolution of AI agents—from simple RAG systems to proactive, embedded assistants
How to escape “proof of concept purgatory” and prioritize AI projects that drive business value
Step-by-step guidance for building reliable, evaluable AI agents

This session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you’re optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.

LINKS

Escaping POC Purgatory: Evaluation-Driven Development for AI Systems - https://www.oreilly.com/radar/escaping-poc-purgatory-evaluation-driven-development-for-ai-systems/
Stop Building AI Agents - https://www.decodingai.com/p/stop-building-ai-agents
How to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.be
My Vanishing Gradients Substack - https://hugobowne.substack.com/
Building LLM Applications for Data Scientists and Software Engineers
https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclub

TIMECODES:

00:00 Introduction and Expertise

04:04 Transition to Freelance Consulting and Advising

08:49 Restructuring Teams and Incentivizing AI Adoption

12:22 Improving Prompting for Timestamp Generation

17:38 Evaluation Sets and Failure Analysis for Reliable Software

23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets

27:38 Software Tools for Evaluation and Monitoring

33:14 Evolution of AI Tools: Proactivity and Embedded Agents

40:12 The Future of AI is Not Just Chat

44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value

50:19 RAG vs. Agents: Complexity and Power Trade-Offs

56:21 Recommended Steps for Building Agents

59:57 Defining Memory in Multi-Turn Conversations

Connect with Hugo

Twitter - https://x.com/hugobowne
Linkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/
Github - https://github.com/hugobowne
Website - https://hugobowne.github.io/

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