# How to Stop Building Products Nobody Wants

## Executive summary

The session details continuous discovery habits—a rigorous process for identifying unmet customer needs and opportunities before building solutions. The core methodology involves structured behavioral interviews (asking customers about past experiences rather than soliciting feedback on an idea). To manage the complexity of these findings, speakers advocate using the Opportunity Solution Tree (OST) framework to ensure all work is tightly coupled to a desired outcome. AI tools are presented as powerful accelerators for historically difficult tasks like synthesizing interview transcripts and providing personalized coaching/feedback, but users must maintain intellectual rigor and understand the limitations of LLMs.

## Key takeaways

- Structured Interviews vs. Idea Feedback: Do not ask customers, "What do you think of my idea?" Instead, ask them to describe a specific time they experienced the problem your product aims to solve (behavioral stories). This yields reliable data that reflects actual behavior, rather than what people *say* they do or wish they did.
- The Opportunity Solution Tree (OST) Framework: The OST is a structured approach designed to guide thinking by ensuring the process starts with the desired 'Outcome.' It mandates moving from Outcome $\rightarrow$ Opportunity Space (unmet needs/pain points) $\rightarrow$ Solutions. This prevents founders from jumping prematurely to solutions based on available technology or assumptions.
- AI for Synthesis and Coaching: LLMs can automate synthesis of transcripts (e.g., identifying key moments/opportunities) and provide personalized coaching feedback (e.g., grading interview techniques). However, the process requires defining clear research goals and understanding that AI output must be validated against human domain expertise to avoid shallow or irrelevant insights.

## Technical details

- Data Synthesis & LLM Pipelines: AI synthesis services can process multiple interview transcripts to generate a draft OST. The system must manage complex data relationships by tracking 'sources' (which interviews contributed to an opportunity) and generating explicit 'change sets' (a diff of moves like merging or splitting opportunities) to maintain provenance and transparency in the evolving tree structure.
- Change Sets and Provenance Tracking: To accurately update an OST using AI, the system must generate a change set—a defined vocabulary of moves (e.g., adding/splitting/merging nodes) that dictates how the input tree is transformed into the output tree. This process requires deterministic generation to ensure the resulting structure is accurate and traceable back to its source data.
- Feedback Loops (Evals): The concept of 'evals' (evaluation loops) is presented as a critical missing discovery habit. It involves designing structured feedback mechanisms—similar to scientific method testing—to test assumptions and validate whether the solution actually solves the identified problem, thereby closing the gap between product design and customer reality.

## Practical implications

- Always start by defining the desired *outcome* (the root of the OST) before brainstorming solutions or opportunities.
- When collecting qualitative data, prioritize asking for detailed stories about past behaviors over general opinions or hypothetical scenarios.
- Use AI tools to automate synthesis and coaching, but treat them as powerful assistants that require human-led research design and validation. Never blindly trust automated outputs.
- Structure your product development process around continuous feedback loops (evals) rather than single, large delivery cycles.

## Topics

Product Discovery, AI/LLM Applications, Build Engineering Process, Behavioral Science, Data Synthesis, AI Evals September 2026 cohort, Opportunity Solution Tree (OST), Vistili

Source: https://www.youtube.com/watch?v=K3cmR_i3dF0