7 min readBy Sourabh Shukla, Founder
The cost of disconnected planning
Teams pay for disconnected planning in three currencies: time spent re-describing the product, tokens spent re-prompting AI coding tools, and rework when generated code violates decisions nobody documented.
- Planning labor: days re-explaining the idea across docs, calls, and tickets
- Token waste: repeated “explain my project” sessions without stable context
- Rework: AI builds against wrong assumptions; teams revert and rebuild
Whole-repo prompts vs task-scoped context
Dumping an entire repository into Cursor or Claude Code increases noise. Without approved architecture and task scope, models optimize for average solutions — not your approved design.
- Large unstructured context → higher cost per useful output
- Missing acceptance criteria → incomplete edge-case handling
- No task sequence → brittle implementation paths and duplicate patterns
What a connected planning workflow changes
FLOWiGANTT is AI-first project intelligence — evaluation, features, architecture, requirements, tasks, and delivery plan in one workflow with approval gates. The context bundle exports that chain as linked Markdown for AI coding tools.
Plan once, then scope each coding session
Export the context bundle after delivery planning. For each task, attach the relevant architecture sections, acceptance criteria, and task row — not the entire project dump.
Use dual estimates for capacity planning
Each engineering task includes traditional and AI-accelerated time estimates. Use both when committing to timelines — acceleration varies by task type and team practice.
What structured context does not do
Connected context improves anchoring and reduces scope drift. It does not eliminate LLM errors, replace code review, or guarantee fixed percentage token savings. Better context per session is the durable win.
- Review generated code against acceptance criteria before merge
- Keep tests and incremental delivery as non-negotiables
- Regenerate upstream planning artifacts when scope changes — cascade keeps downstream consistent
Before vs after prompt structure
Scenario: Implement user authentication API endpoints
Add JWT authentication to our API. Use Redis for rate limiting. Follow best practices.Using @architecture-{projectId}.md and @tasks-{projectId}.md:Implement the authentication flow defined in the approved architecture.Match acceptance criteria from the PRD for session handling and error payloads.Scope work to the task sequence in the planning workflow — do not invent new endpoints.Start with the planning workflow
Build the connected graph first. Reuse the same project intelligence across Cursor, Claude Code, Copilot, and your issue tracker.
Recommended file set
- architecture-{projectId}.md — approved system structure and API boundaries
- prd-{projectId}.md — acceptance criteria and behavioral constraints
- tasks-{projectId}.md — scoped implementation sequence with file hints