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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

 
 
✓ Featured Content

AI Coding Videos

A curated playlist of specific YouTube content.

Search Results

24 Feb 2026

Step 2: Turn backlog items into “buildable slices” (small, testable, valuable)

Author: Rod Claar  /  Categories: Software Developer Learning Path  /  Rate this article:
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What Makes a Slice “Buildable”

A buildable slice:

  • Produces user-visible behavior

  • Can be tested independently

  • Meets the Definition of Done

  • Contributes to a measurable outcome

  • Can be completed within a Sprint

If it requires multiple handoffs or partial completion, it is too large.


Practical Slicing Patterns

Use these techniques deliberately:

1. Workflow Step
Deliver one end-to-end step before the full process.

2. Happy Path First
Implement the simplest scenario before edge cases.

3. Single Rule Variation
Support one business rule before adding complexity.

4. Data Scope
Enable one user type or limited dataset first.

5. Risk-First Slice
Implement the most uncertain part early.


Example

Oversized Story:

Build reporting dashboard for release readiness.

Possible Slices:

  1. Display backlog readiness score for top 10 items

  2. Flag items missing acceptance criteria

  3. Highlight cross-team dependencies

  4. Show Sprint goal completion trend

  5. Export summary as PDF

Each slice is independently demonstrable.


Acceptance Criteria Discipline

Every slice should include:

  • Clear behavioral expectation

  • Explicit inputs and outputs

  • Testable conditions

If acceptance criteria contain ambiguity, refinement is incomplete.


Exercise

  1. Select one oversized backlog item.

  2. Split it into 3–5 buildable slices.

  3. Write acceptance criteria for each slice.

  4. Confirm each slice could be:

    • Built

    • Tested

    • Demonstrated

    • Released

If not, refine further.

Smaller slices reduce variability, improve predictability, and increase learning velocity.

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