Phase 1: Client Discovery & Problem Decomposition · 55 min · Python · Markdown · Mermaid

Turning Vague Problems into Technical Specs

The spec is the contract. The code is the implementation.

Hiring signal: The take-home project and decomposition case study both test spec writing: candidates who produce a structured technical spec (problem statement, architecture diagram, data requirements, evaluation plan, timeline, risks) alongside their code pass. Candidates who submit code without documentation fail. The ability to map business metrics to technical metrics is the translation skill that defines the FDE role.

What you will learn

Write a technical spec using the FDE template: problem, solution, architecture, data, eval, timeline, risks
Map business metrics to technical metrics: 'reduce resolution time' → 'classification accuracy on 10k queries'
Perform AI insertion point analysis: where in the client workflow does AI add value vs. risk?
Make build vs. buy decisions in the spec: APIs vs. custom vs. fine-tuning

What You'll Learn

This lesson takes approximately 55 min. By the end, you will be able to:

Write a technical spec using the FDE template: problem, solution, architecture, data, eval, timeline, risks
Map business metrics to technical metrics: 'reduce resolution time' → 'classification accuracy on 10k queries'
Perform AI insertion point analysis: where in the client workflow does AI add value vs. risk?
Make build vs. buy decisions in the spec: APIs vs. custom vs. fine-tuning

The Problem

The gap between "we need better search" and a technical spec with acceptance criteria, data schemas, and evaluation metrics is where FDEs create most of their value. This lesson walks through the full transformation: taking a vague client problem, applying the decomposition framework, and producing a spec that an engineering team can build against without ambiguity.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The FDE Technical Spec Template, Mapping Business Metrics to Technical Metrics, AI Insertion Point Analysis, Build vs. Buy Decisions, Practical Application, What Hiring Managers Look For, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.

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