docs: import qa workflow knowledge from grilling session

docs/improvement-goals.md

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+---
+last_updated: 2025-07-15
+tags: [improvement, goals, priorities, yara724]
+source: import-knowledge
+---
+
+# Improvement Goals — Yara724 QA Workflow
+
+## Primary Goal
+
+Automate repetitive work — addresses the two biggest time sinks (bug diagnosis at 40% and API investigation at 25%).
+
+## Secondary Goal
+
+Free time for other projects, improve technical understanding.
+
+## Long-Term Objective
+
+Build an AI-assisted QA workflow that combines product knowledge, API analysis, documentation, testing, and bug investigation into a streamlined process. The goal is to reduce manual effort, improve consistency, and allow focus more on high-value engineering work rather than repetitive documentation.
+
+---
+
+## Priority 1: State Preparation Automation (FIRST)
+
+A tool that chains API calls to bring a Case to any target state, supporting:
+
+- Fresh Case creation each time (default)
+- Reuse of existing Cases from MongoDB (optional)
+- All target states: ready for Expert review, ready for Claim submission, ready for Insurer review, etc.
+- Target states cover the full Blame → Claim lifecycle
+- Interface TBD — options: Bruno collection, standalone script, CLI tool
+
+- [ ] Define success metrics for Priority 1
+
+## Priority 2: Bug Diagnosis Assistance (SECOND)
+
+AI that takes a network request/response (from DevTools) and suggests likely root cause, reducing the 40% bug investigation bucket.
+
+**Current bug reporting format**: QA pastes a URL + screenshot into Jira. Does not paste full request/response body or expected vs actual comparison — this is the gap AI can fill.
+
+- [ ] Define success metrics for Priority 2
+
+---
+
+## Improvement Opportunities (from workflow assessment)
+
+1. Standardized Bruno/Postman collections organized by business flows
+2. Better use of environments and variables to reduce repetitive authentication work
+3. Documentation of business flow → API mappings
+4. Reusable test data and state preparation procedures
+5. AI-assisted API discovery and workflow navigation
+6. AI-assisted root cause analysis support
+7. A centralized QA knowledge base to preserve project knowledge across time and projects
+
+---
+
+## Open Questions (from original working doc)
+
+### Process Understanding
+- What percentage of time is spent on each activity?
+- Which activities are repetitive?
+- Which activities require human judgment?
+- Which activities can be automated?
+- Which activities can be standardized using templates?
+
+### Documentation
+- Can screenshots be automatically annotated?
+- Can UI differences be detected automatically?
+- Can Jira tickets be generated directly from screenshots?
+- Can API changes be inferred automatically from Swagger?
+
+### Backend Analysis
+- Can request/response differences be detected automatically?
+- Can API regressions be detected before manual testing?
+- Can Swagger documentation be compared automatically between versions?
+
+### Testing
+- Which regression tests should be automated?
+- Which tests should remain manual?
+- Can Bruno/Postman collections generate parts of Jira tickets automatically?
+
+### AI Integration
+- Can AI generate the first draft of every Jira task?
+- Can AI determine whether a bug belongs to the frontend or backend?
+- Can AI summarize long Jira tickets?
+- Can AI analyze DevTools network traffic?
+- Can AI explain unfamiliar code sections?