Session Continuity

Never lose context between AI sessions. Pick up exactly where you left off—days, weeks, or months later—without re-explaining anything.

The Problem

Traditional AI coding sessions have a critical flaw: context loss.

When you end a session and return later, you face:

• Re-explaining the entire project
• Reminding the AI of past decisions
• Rebuilding the mental model
• Losing track of work in progress
• Forgetting why choices were made

This isn't just inconvenient—it's a productivity killer.

The Solution

The AI Context System solves this through structured documentation that preserves:

1. Current State (STATUS.md)

What's happening right now:

• Active tasks and their status
• Current blockers
• Next session start point
• Auto-generated Quick Reference

Example:

## Work In Progress

**Current Task:** Implementing user authentication
- ✅ Set up database schema
- ✅ Created User model
- 🔄 Building login endpoint (in progress)
- ⏳ Add password hashing (next)

**Blocker:** Need to decide between JWT and session-based auth

When you return, /review-context shows this immediately.

2. Session History (SESSIONS.md)

What happened and why:

• Session-by-session timeline
• AI mental models at each point
• Problem-solving approaches
• What changed and why
• Git operations logged automatically

Example:

## Session 3 | 2025-10-22 | Authentication Implementation

**TL;DR:** Implemented JWT-based authentication with refresh tokens

### Mental Models
**Current understanding:**
- Using JWT for stateless auth (decision: prefer horizontal scaling)
- Refresh tokens stored in Redis (7-day expiry)
- Access tokens short-lived (15 min)
- HTTPS-only cookies for security

### Problem-Solving Approach
- Researched JWT vs sessions (sessions require sticky sessions)
- Decided on JWT for better load balancing
- Implemented refresh token rotation for security

3. Decision Rationale (DECISIONS.md)

Why choices were made:

• Technical decisions with reasoning
• Alternatives considered
• Constraints and tradeoffs
• When to reconsider

Example:

## DEC-003: Use JWT for Authentication

**Decision:** Implement JWT-based authentication instead of session-based

**Context:** Building API that will scale horizontally with multiple servers

**Options Considered:**
1. Session-based auth (requires sticky sessions or shared session store)
2. JWT with refresh tokens (stateless, better for horizontal scaling)

**Decision:** JWT with refresh tokens

**Rationale:**
- Horizontal scaling without sticky sessions
- Stateless auth = simpler load balancing
- Refresh token rotation provides security
- Industry standard for APIs

How It Works

Daily Workflow

Start of session:

/review-context

Shows:

• Quick Reference (project overview)
• Current work in progress
• Recent decisions
• Version check

During work:

/save  # Every 30-60 minutes (2-3 min)

Updates:

• STATUS.md with current state
• Auto-generated Quick Reference
• Work in progress

End of session:

/save-full  # Before breaks (10-15 min)

Creates:

• Complete SESSIONS.md entry
• Mental model capture
• Decision rationale
• Git operation log

Resuming Later

Days, weeks, or months later:

/review-context

What you see:

1. Quick Reference - Project at a glance
2. Last session summary - What happened
3. Work in progress - Where to pick up
4. Recent decisions - Context for current work

Result: You're oriented in 2-3 minutes instead of 20-30 minutes.

Real-World Example

Without Session Continuity

Day 1: Implement authentication

• AI builds JWT system
• Makes key decisions
• Session ends

Day 8: Resume work

• "What were we doing?"
• "Why JWT instead of sessions?"
• "What's the token expiry?"
• Re-explain everything (20 minutes)
• Risk of inconsistent decisions

With Session Continuity

Day 1: Implement authentication

/save-full  # 10 minutes

Day 8: Resume work

/review-context  # 2 minutes

Sees:

• Current: "Building login endpoint (in progress)"
• Blocker: "Need to decide between JWT and sessions"
• Decision: "DEC-003: Use JWT (horizontal scaling)"
• Mental model: Complete auth strategy

Result: Pick up exactly where you left off.

Key Benefits

1. Zero Context Loss

• AI remembers everything
• No re-explaining
• Consistent decision-making

2. Fast Resume

• 2-3 minute orientation
• Clear start point
• Full context available

3. Long-Term Projects

• Works across months
• Handles complexity
• Maintains consistency

4. Multiple AI Agents

• Seamless handoffs
• Peer review with context
• Collaborative development

Best Practices

Save Frequently

/save  # Every 30-60 minutes

• Captures incremental progress
• Safety net for unexpected interruptions
• Quick (2-3 minutes)

Full Saves at Boundaries

/save-full  # Before breaks, handoffs

• Comprehensive mental model
• Decision rationale
• Complete session record

Review at Start

/review-context  # Every session start

• Instant orientation
• Verifies documentation current
• Checks for updates

Trust the System

• Document even "obvious" decisions
• Capture mental models honestly
• Future you will thank present you

Success Metric

"I can end any session abruptly, start days later, run /review-context, and continue exactly where I left off."

When this is true, you have perfect session continuity.

Next Steps

• Externalized Context - Make AI reasoning visible
• Mental Models - Capture AI understanding
• STATUS.md Guide - Current state documentation
• SESSIONS.md Guide - Session history