Project Overview

I built Release Notes Manager to replace a fully manual, engineer-dependent release documentation workflow with a scalable, AI-driven system. The tool ingests Jira stories, sprint metadata, and internal changes, and transforms them into clean, structured release notes that can be published directly to the Docusaurus documentation site with a single click — no code or markdown editing required.

The platform supports:

• Jira story import + automatic package detection

• AI summarization with strict formatting constraints

• Multi-package & multi-sprint selection

• JSON schema generation for predictable structure

• Editable preview with re-ordering

• One-click PDF export for external sharing

Goal: Remove repetitive manual work, ensure consistency across CAP/BASE/RMP releases, and give non-technical teams full control of publishing.

Problem

Before this system:

• Release notes were written manually

• Every engineer wrote stories differently

• Jira descriptions were inconsistent and technical

• Managers had to rewrite content every sprint

• Information was scattered across Jira, Slack, commits

• Exporting PDFs required copy/paste and formatting

• No unified template across product packages

The result was hours of wasted effort every sprint, inconsistent communication, and frequent last-minute rewrites.

Users / Audience

• Release Managers

• Product Owners

• QA Leads

• Engineering Managers

• Implementation Specialists

User Needs

• Generate accurate summaries instantly

• Combine multiple sprints or packages

• Publish directly to documentation without writing markdown

• Export production-ready PDFs

• Maintain consistent structure across monthly releases

• Avoid manual rewriting

• Trust AI outputs with predictable formatting

Goals

• Reduce documentation creation effort

• Standardize release formats across all teams

• Automate summarization & categorization

• Support all internal package types (CAP, BASE, RMP)

• Produce clean JSON structures for publishing

• Enable editing, re-ordering, and regeneration with minimal friction

Architecture Overview

1. Release Selection Panel

• Select package (CAP / BASE / RMP)

• Choose sprints (current / previous / future)

• Add to export list

• Reorder or remove entries

2. Jira Integration Layer

• Fetch stories for selected sprints

• Extract titles, descriptions, labels, change type

• Organize by sprint and package

3. AI Summarization Engine

• Cleans Jira input

• Removes internal jargon

• Auto-categorizes content
  New Features / Enhancements / Fixes / Breaking Changes

• Produces human-readable release notes

• Generates strict JSON schema for consistent publishing

4. JSON Preview Layer

• Displays final structured data

• Allows refinement or regeneration

• Ensures no surprises before publishing

5. Publish + Export Pipelines

• One-click publish → pushes release notes into Docusaurus

• PDF exporter → generates branded release notes

• Supports multi-package & multi-sprint bundles

Process

1. Research

• Reviewed 12+ previous release note documents

• Identified inconsistencies & rewrite patterns

• Interviewed Release Managers and Product Owners

• Documented bottlenecks caused by manual workflows

2. Early Concepts

• Multi-tab workflow (Generate / Configuration / Export)

• Smart grouping of Jira stories

• AI prompt customization

• Drag-and-drop export management

3. UX Design

• Built a clear hierarchy: Package → Sprint → Changes

• Added JSON preview for transparency

• Designed predictable AI output structure

• Ensured screens supported high-volume release cycles

4. High Fidelity UI

• Release generator

• Story import

• JSON preview

• Export list panel

• PDF generation flow

5. Development & Integration

• AI summarizer with deterministic behavior

• Jira API (stubbed locally for testing)

• Branded PDF generator

• Docusaurus publishing pipeline

6. Validation

• Tested with 10+ past sprints

• Validated summaries with Product team

• Ensured consistent, repeatable exports

Impact

Quantitative

• Release notes creation: 2–3 hours → 5–7 minutes

• Consistency improved ~90%

• Manager rewrite time reduced drastically

• Multi-package export: 45 mins → <1 min

Qualitative

• Clean, branded outputs every time

• Clearer cross-team communication

• QA gained instant visibility into changes

• Product teams stopped rewriting engineer-written text

Challenges

• Ensuring AI summaries remained accurate to Jira stories

• Handling different package structures (CAP / BASE / RMP)

• Designing AI guardrails to prevent hallucinations

• Making JSON readable for non-technical users

• Enforcing branding in PDF output

Reflection

This project strengthened my ability to design AI-assisted documentation workflows that balance automation with human oversight. I learned how to:

• Build predictable, reviewable AI outputs

• Create UX flows that non-technical teams can trust

• Structure long-form AI content safely

• Replace engineering-heavy processes with scalable systems

  
  

Flowchart

Screens