---
title: "Slides: Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS"
category: "slides"
video_id: "IddXPepIAS4"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS

## Source Video
[Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS](https://www.youtube.com/watch?v=IddXPepIAS4)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/IddXPepIAS4/slide-001.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/IddXPepIAS4/slide-001.html)
- AI slide classifier: `title_card` confidence `0.98`
- Text source: agent_vision.

Slide text:

> What is Spec-Driven Development?
> And how can it make code faster?

![[assets/slides/IddXPepIAS4/slide-002.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/IddXPepIAS4/slide-002.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Spec-Driven Development
> Structured specifications are created before any code is written.

![[assets/slides/IddXPepIAS4/slide-003.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/IddXPepIAS4/slide-003.html)
- AI slide classifier: `content_slide` confidence `0.9`
- Text source: agent_vision.
- OCR decision: ready — Multi-column diagram with smaller labels and embedded logos; OCR can recover the full on-slide text more efficiently than manual transcription.

Slide text:

> A Solution to the Integrations Problem

![[assets/slides/IddXPepIAS4/slide-004.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/IddXPepIAS4/slide-004.html)
- AI slide classifier: `content_slide` confidence `0.97`
- Text source: none.
- OCR decision: ready — Dense code/editor screenshot with small UI text and multiple panes is better handled by OCR.
- Slide text: not surfaced (`none` by AI classifier).
![[assets/slides/IddXPepIAS4/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/IddXPepIAS4/slide-005.html)
- AI slide classifier: `title_card` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Thanks!
> LinkedIn: Erik Hanchett
> X/Bluesky: Erikch


Classification audit: `raw/sources/slide-ai-classification/slides/IddXPepIAS4/audit.json`

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.