Free Lesson

Stop Paying Full Price for LLM Classification

Part of AI Product Engineering

Hosted by Shreya Shankar and Hamel Husain

Thu, Jul 30, 2026

5:00 PM UTC (45 minutes)

Virtual (Zoom)

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AI Evals For Engineers & PMs
Hamel Husain and Shreya Shankar
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What you'll learn

Find where you're overpaying

Spot the requests where you're handing a hard model an easy job.

Build a cheaper-first pipeline

Use an agent to find cheaper stand-ins and run those first, calling your best model only when the cheap path is unsure.

Lock in an accuracy floor

Set an accuracy target against the model you trust and tune the workflow to stay above it.

Why this topic matters

A lot of LLM products spend a fortune sending every request to an expensive model. Often the model is making a simple decision, like which model should handle a request or whether a document is relevant. These are classification problems, and because you can check the answer, you can make them cheaper and faster without losing quality. We'll show you how, on real data.

You'll learn from

Shreya Shankar

AI researcher; incoming CS Professor at Carnegie Mellon

Shreya builds open-source systems for AI-powered data processing. She holds a PhD in Electrical Engineering & Computer Science from UC Berkeley and, in 2027, will join Carnegie Mellon University as an Assistant Professor of Computer Science. Shreya created DocETL, an open-source system for analyzing unstructured text at scale, deployed across journalism, law, medicine, policy, finance, and urban planning. Her research has been published at top computer science venues including VLDB, SIGMOD, and UIST (including a Best Paper award), and has influenced systems at Snowflake, BigQuery, LangChain, and OpenAI. Before her PhD, Shreya worked as a machine learning and data engineer at startups. She holds a BS in Computer Science from Stanford University.

Hamel Husain

ML Engineer with 20+ years of experience

Hamel Husain is a ML Engineer with 20+ years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies build AI products.

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