Ellen Brandenberger, Stack Overflow - Just Now Possible

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When ChatGPT launched, Stack Overflow faced a cataclysmic shift: developer behavior was changing overnight. In this episode, Teresa Torres talks with Ellen Brandenburger, former product leader at Stack Overflow, about how her team navigated the disruption, prototyped AI features, and eventually built an entirely new business line.

Ellen shares the inside story of Overflow AI—from the first scrappy prototypes of conversational search, through multiple iterations with semantic search and RAG, to the tough decision to roll the product back when it couldn’t meet developer standards. She also explains how Stack Overflow turned a looming threat into opportunity by creating technical benchmarks and licensing its Q&A corpus to AI labs.

This episode offers a rare look at what it really takes to adapt when a platform-defining shift hits—and what product managers, designers, and engineers can learn about prototyping, evaluating quality, and building in uncertainty.

Guest:

Ellen Brandenburger – Product leader and coach; former head of product at Chegg Skills and Stack Overflow’s data licensing team.

What we cover in this episode:

• How Ellen joined Stack Overflow just two weeks before ChatGPT launched, reshaping the company’s future overnight
• The creation of Overflow AI: a team tasked with exploring “what’s just now possible” for developers
• Four iterations of conversational search:
  • V1: a chat UI on top of keyword search
  • V2: semantic search to handle natural questions
  • V3: fallback to GPT-4 for gaps in Stack Overflow’s corpus
  • V4: adding RAG for attribution and transparency
• Why attribution and transparency were critical for developer trust
• How the team used simple spreadsheets and subject-matter experts to evaluate answer accuracy, relevance, and completeness
• Why Stack decided to sunset conversational search despite heavy investment—what they learned and why it wasn’t wasted
• The pivot to data licensing: how Stack Overflow leveraged its 14M+ Q&A corpus to power LLM training and benchmarks
• Building industry benchmarks with subject-matter experts to prove Stack data improved LLM accuracy and relevance

Key lessons:

• Take one bite of the apple at a time—prototype, learn, iterate
• Product in the AI era means managing probabilities, not certainties

Links & References:

• Ellen Brandenburger on LinkedIn
• The Changing State of the Internet and Related Business Models
• ProLLM: LLM benchmarks for real-world use-cases

Full Transcript

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