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Learning Path

AI on a Development Team

Who it’s for: Developers, testers, and tech leads who want practical, sprint-ready ways to use AI to build faster without sacrificing quality.

Outcomes

  • Use AI to turn vague work into clear, testable stories and acceptance criteria the team can build from.
  • Accelerate coding with guardrails: prompts that reinforce TDD, code review quality, and consistent patterns.
  • Improve delivery reliability by using AI for risk surfacing, edge cases, and “definition of done” readiness checks.

Path Steps

Work through these steps in order. Each one links to a specific EasyDNNnews article/video post.

8 steps
1
Step 1: How AI fits into a dev team (without chaos)

You’ll learn where AI helps most (planning, building, testing, reviewing) and how to keep the team in control.

Do this List 3 recurring “time sinks” in your sprint and pick one to target with AI assistance first.
5
Step 5: Code generation with guardrails

You’ll learn how to constrain AI output to your architecture, conventions, and security requirements.

Do this Create a “project rules” snippet (stack, patterns, naming, linting) and reuse it in every coding prompt.
7
Step 7: Test data, mocking, and troubleshooting with AI

You’ll learn how to generate realistic test data and isolate failures faster with structured debugging prompts.

Do this Paste a failing test + stack trace and ask AI for the top 3 hypotheses with “how to prove/kill each.”

Steps - Free

Steps - Members

 
 
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AI Coding Videos

A curated playlist of specific YouTube content.

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24 Feb 2026

Step 3: Turn outcomes into backlog slices (without giant stories)

Author: Rod Claar  /  Categories: Product Owner Learning Path  /  Rate this article:
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Start With the Outcome

Revisit your measurable outcomes from Step 1.

Example outcome:

  • Increase Sprint goal completion from 60% to 85%

Now ask:

What smallest usable behavior would move this metric?

Not:

  • “Build planning module”

  • “Create reporting dashboard”

Instead:

  • Show backlog readiness score for top 10 items

  • Highlight missing acceptance criteria automatically

  • Flag dependencies across teams

Each slice should enable a real decision.


Use These Slicing Techniques

1. Workflow Slicing
Deliver one step of the workflow end-to-end.

2. Rule Variations
Implement the simplest rule first. Add complexity later.

3. Data Subset
Support one user type or one scenario before expanding.

4. Risk First
Build the part with the highest uncertainty early.


Definition Check

A properly sliced backlog item:

  • Has clear acceptance criteria

  • Produces observable user behavior

  • Can be demonstrated

  • Can be tested

  • Moves at least one measurable outcome

If it takes multiple Sprints, it is still too large.


Practical Heuristic

If the story contains “and,” split it.

Example:

System validates input and generates report
That is two slices.


Small slices reduce cognitive load, improve forecasting accuracy, and surface feedback faster.

That is how outcomes become delivery.

Precision here compounds across every Sprint.

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