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Collaborative Context Hub for LLMs

In this use case, Readit serves as a shared context hub where multiple team members can contribute, update, and access contextual information for a particular project, feature, or client. Each team member can use their preferred LLM tool—like ChatGPT, Gemini, Claude, or Codex—but they all share and enrich the same Readit link.

How It Works

Section titled “How It Works”

1. Starting the Context

Section titled “1. Starting the Context”

One team member begins by creating a Readit containing initial context and decisions. They use this Readit link while chatting with an LLM to analyze or brainstorm about the feature.

2. Collaborative Updates

Section titled “2. Collaborative Updates”

Another team member can take the same Readit link and continue the analysis in their LLM of choice. They add new insights or decisions back into the Readit, keeping the context up-to-date.

3. Development and Testing

Section titled “3. Development and Testing”

The developer can use the same Readit to understand the full context and make informed architectural decisions. They might also add technical notes or documentation back into the Readit once development is complete.

4. Support and Assistance

Section titled “4. Support and Assistance”

The support team can access the same Readit to get all the relevant background information and provide more accurate assistance to users.

5. Easy Contribution

Section titled “5. Easy Contribution”

Team members can quickly add information to the Readit by pasting a link to a chat, sending an email to a unique address for that Readit, or using a simple interface to append new notes. This makes it easy for anyone to keep the context fresh and complete.

Example: Feature Development Lifecycle

Section titled “Example: Feature Development Lifecycle”

Initial Planning Phase

Section titled “Initial Planning Phase”

Product Manager creates:

https://readit.md/team/user-dashboard-v2


Files:
├── main.md              # Feature requirements and goals
├── user-research.md     # Research findings and user feedback
├── competitive-analysis.md # Market analysis
└── success-metrics.md   # KPIs and success criteria

The PM uses this Readit with ChatGPT to refine requirements and explore edge cases.

Design Phase

Section titled “Design Phase”

UX Designer takes the same Readit link and adds:

├── design-decisions.md  # Design rationale and choices
├── wireframes.md        # Links to design files
├── accessibility.md     # A11y requirements
└── user-flows.md        # User journey mappings

The designer uses this enriched context with Figma AI or Claude to validate design decisions.

Development Phase

Section titled “Development Phase”

Developer accesses the full context and adds:

├── technical-approach.md # Architecture decisions
├── api-design.md        # API specifications
├── database-changes.md  # Schema modifications
└── implementation-notes.md # Development insights

The developer uses this comprehensive context with GitHub Copilot or Cursor AI.

Testing & QA Phase

Section titled “Testing & QA Phase”

QA Engineer uses the complete Readit to understand:

  • Original requirements
  • Design intentions
  • Technical implementation details
  • Known edge cases

They add:

├── test-strategy.md     # Testing approach
├── edge-cases.md        # Discovered edge cases
└── bug-reports.md       # Issues found during testing

Integration with Project Management

Section titled “Integration with Project Management”

Readit complements tools like Jira by serving as the central knowledge repository:

  • Jira: Tracks tasks, issues, and progress
  • Readit: Holds the living context that evolves with every conversation and decision

Example Integration

Section titled “Example Integration”
Jira Epic: User Dashboard V2
├── Readit Link: https://readit.md/team/user-dashboard-v2
├── Task: Design wireframes → Updates design-decisions.md
├── Task: Implement API → Updates technical-approach.md
└── Task: User testing → Updates user-research.md

Each Jira task references the same Readit, ensuring everyone has access to the complete, evolving context.

Collaboration Patterns

Section titled “Collaboration Patterns”

Cross-Functional Alignment

Section titled “Cross-Functional Alignment”
  • Marketing uses the Readit to understand feature benefits for messaging
  • Sales accesses customer requirements and competitive positioning
  • Customer Success reviews the context for training and support preparation

Knowledge Transfer

Section titled “Knowledge Transfer”

When team members join or leave:

  • New joiners get instant access to complete project history
  • Departing members leave their insights captured in the Readit
  • No knowledge is lost in handoffs

Multi-LLM Workflow

Section titled “Multi-LLM Workflow”
Team Member A (ChatGPT) → Updates Readit → Team Member B (Claude)
     ↓                                              ↓
Adds analysis to context  ←———————————————————— Continues analysis

Each person uses their preferred AI tool, but everyone benefits from the shared, growing context.

Benefits

Section titled “Benefits”
  • 🔄 Continuous Context Building – Knowledge accumulates across all team interactions
  • 🤝 Cross-LLM Compatibility – Works with any AI tool that accepts markdown input
  • 📈 Evolving Intelligence – Context gets richer with each contribution
  • 🎯 Unified Understanding – Everyone works from the same comprehensive information
  • ⚡ Instant Onboarding – New team members get complete context immediately
  • 🔗 Tool Integration – Links seamlessly with existing project management workflows

When to Use

Section titled “When to Use”
  • Multi-phase projects with handoffs between team members
  • Cross-functional features requiring input from multiple disciplines
  • Long-running projects where context needs to be preserved over time
  • Teams using different AI tools but needing shared understanding
  • Client projects where multiple stakeholders contribute insights
  • Knowledge-intensive work where decisions build upon previous discussions