When we launched AI Tickets in October, we knew we had something powerful. But we also knew it wasn't perfect. Tickets sometimes missed context, referenced non-existent components, or left gaps in implementation details.
So we set out on a mission: improve ticket quality from 6.5/10 to production-ready. Through iterative improvements over the following months, we achieved 9.0/10. Here's how.
The Starting Point
Our initial release worked, but quality assessments revealed gaps. Tickets scored 6.5/10 with common issues:
- 4 gaps per ticket on average — missing implementation details
- Generic code references — "ticketService" instead of actual file paths
- Fabricated components — suggesting files that didn't exist
- Vague task descriptions without evidence citations
We needed a systematic approach to improve quality. So we built one, version by version.
The Journey: Atomic v1.2 → Atomic v1.5
We introduced taxonomy-based filtering to understand what type of feature was being requested. This allowed us to match requirements with the right code patterns.
We enriched prompts with rich metadata: API endpoints, function signatures, class structures, and tech stack information. The LLM now had much more context to work with.
We added automatic detection of external dependencies: APIs, databases, cloud services, and third-party libraries. Tickets now include comprehensive integration context.
Infrastructure That Powers It
Quality improvements weren't just about better prompts. We also rebuilt the foundation that makes AI Tickets possible.
Database Indexing
We redesigned our code indexing system with a comprehensive schema, enabling richer metadata extraction and more reliable code analysis.
File Taxonomy
Enhanced classification system that understands file categories, frameworks, and architecture layers.
The Results
What this means for developers using AI Tickets:
- Dramatically fewer gaps — tickets now include comprehensive implementation details
- Actual code references — no more fabricated components or generic paths
- Integration awareness — automatic detection of APIs, databases, and cloud services
- Better task breakdown — more granular, actionable implementation steps
What's Next
We're not done. The journey from 6.5 to 9.0 taught us that systematic, iterative improvement works. We're continuing to refine AI Tickets with:
- API endpoint prioritization for better context matching
- Dependency analysis for more complete task breakdowns
- Layer-aware completeness to prevent missing implementation steps