In today’s fast-paced world of software development, the mantra “move fast and break things” is giving way to “move smart and ship clean.” At my company, our Tech Leads have transformed how we build products by embedding AI as a practical accelerator at every stage of the journey—from the first spark of an idea all the way through to deployment. In this three-part series, I’ll illustrate how he does it, starting with Requirement Analysis & User Stories, Technical Design, and Task Management & Planning.
1. Requirement Analysis & User Stories
The Challenge
Requirements often start as high-level ideas in meetings or a half-filled Google Doc. Turning those into clear, actionable user stories can be a painful game of telephone—stakeholder intent gets lost, acceptance criteria go vague, and developers end up guessing.
How We Use AI
We feed AI models with transcribed meeting notes or bullet-point idea lists and prompt them to generate structured epics and user stories. For example, we’ll use:
Prompt:
“Convert these meeting notes into three user‐story statements with acceptance criteria:
- Need to track user carbon footprint per transaction
- Allow users to set monthly eco‐goals
- Send alerts when near goal limit”
The reply we receive:
As a user, I want to see my carbon footprint per transaction so that I can understand the environmental impact of my spending.
Acceptance Criteria:
- Display carbon footprint in grams of CO₂ alongside each transaction.
- Provide a link to a detailed breakdown view.
As a user, I want to set monthly eco‐goals so that I can manage my carbon budget effectively.
Acceptance Criteria:
- Allow the user to define a target in kg of CO₂ per month.
- Store and display the current progress toward the target.
As a user, I want to receive an alert when I approach my monthly eco‐goal so that I can adjust my spending.
Acceptance Criteria:
- Trigger a push notification when 90% of the eco‐goal is reached.
- Provide a link to suggestions for greener spending alternatives.
We then import these into Notion AI or our backlog tool. The result? Faster backlog grooming and clearer stories that everyone—product managers, developers, and QA—understands from day one.
2. Technical Design
The Challenge
Sketching system architecture and validating design decisions by hand can take days. Diagrams get outdated, edge cases slip through, and you end up with patchwork solutions.
How We Use AI
Our Tech Lead uses Mermaid combined with AI-powered plugins (e.g., the “Mermaid-AI” extension) to automatically generate up-to-date diagrams. For instance, we’ll write a quick prompt:
Prompt: “Generate a sequence diagram for payment processing with fraud check, carbon footprint logging, and ledger update.”
- participant U as User
- participant F as Frontend
- participant B as Backend
- participant C as CarbonService
- participant L as LedgerService
- U->>F: Initiate Payment
- F->>B: /api/payment with token
- B->>C: Log carbon footprint
- C-->>B: Carbon footprint recorded
- B->>L: Debit user account
- L-->>B: Account debited
- B-->>F: Payment success
- F-->>U: Show confirmation
Within seconds, we have a clean, version-controlled diagram. We can then iterate with prompts like:
“Highlight error-handling for failed fraud checks.”
That auto-generates the updated paths. The net effect is accelerated solution design and better documentation—all stored alongside code in our Git repo.
3. Task Management & Planning
The Challenge
Breaking down features into tasks, estimating timelines, and identifying dependencies is notoriously error-prone—especially when projects scale or new people join teams.
How We Use AI
We leverage Linear AI Assist or Jira Smart Assistants to auto-categorize tasks, suggest story-point estimates, and flag dependencies. Here’s a typical workflow:
Input: “Create tasks for Payment Service feature,” plus a list of user stories.
AI outputs:
Create API endpoint for POST /payments (5 SP, backend)
- Integrate CarbonService SDK (3 SP, backend)
- Build frontend component for payment form (5 SP, frontend)
- Write unit tests for new endpoints (3 SP, QA)
- Review: Tech Lead adjusts any estimates or reassigns priorities.
The result? Smoother sprint planning, reduced PM overhead, and fewer surprises mid-sprint. Developers log on Monday and know exactly what to tackle, and PMs can predict delivery dates with more confidence.
Wrapping Up Part 1
In these first three stages—Requirement Analysis & User Stories, Technical Design, and Task Management & Planning—our Tech Lead uses AI not as fluff, but as a practical accelerator that:
- Speeds up routine work.
- Ensures consistency across stories and designs.
- Frees the team to focus on deep engineering and product strategy.
In Part 2, we’ll dive into Development, Code Review & Debugging, Testing & QA, and Documentation—showing you how we keep code clean, catch bugs early, and ship with confidence.
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