Your AI Needs Context Management (And You Do Too)

• Jader Correa

The Problem Nobody’s Talking About

You’re 3 hours into a new AI assistant session. You’ve already explained:

  • Your microservices architecture (again)
  • Which services use which databases (for the 10th time this week)
  • The deployment process (because it forgot)
  • What you’re actually trying to build (starting from scratch)

Sound familiar?

Every. Single. Session.

With every AI tool (Warp, Cursor, ChatGPT, Claude), you’re playing the same game:

“Let me copy-paste this architecture doc… wait, where did I put it? Oh right, it’s in Notion. No wait, we updated it in Slack. Actually, is this version outdated?”

Meanwhile, your teammate is explaining a completely different architecture to their AI assistant.

The Hidden Cost

Let’s do the math:

  • 10 minutes setting up context per session
  • 5 AI sessions per day (switching tools, new features, debugging)
  • 50 minutes/day just explaining your own project
  • ~4 hours/week
  • ~200 hours/year per developer

For a 10-person team, that’s 2,000 hours/year = $200,000 in wasted time (at $100/hour).

And that’s before counting:

  • Inconsistent answers (each dev has different context)
  • Onboarding nightmare (2 weeks for new devs to grok the system)
  • Context drift (your AI doesn’t know about last month’s refactor)
  • Tool switching penalty (different context in Warp vs Cursor vs Claude)

The Solution: Treat Context Like Code

Here’s a radical idea: What if project context was version-controlled, team-shared, and tool-agnostic?

Just like you wouldn’t copy-paste code between developers, why copy-paste context between AI sessions?

Introducing Universal Context Management

# One time setup
cd ~/your-project
gptcode context init

# Edit context (like editing code)
vi .gptcode/context/shared.md

# Sync to ALL your AI tools
gptcode context sync
# ✅ Warp
# ✅ Cursor
# ✅ Clipboard (for ChatGPT/Claude web)

That’s it. Single source of truth. Version-controlled. Team-shared.

What You Get

1. Zero-effort context loading

# Every Warp session
gptcode context sync  # ← 1 second
# vs
# 10 minutes of copy-pasting and explaining

2. Team consistency

# Developer A updates architecture
echo "## Redis Layer\nCaching strategy..." >> .gptcode/context/shared.md
git commit -am "Add Redis context"
git push

# Developer B gets it automatically
git pull
gptcode context sync
# ✅ Their AI now knows about Redis too

3. Tool agnosticism

Same context works in:

  • ✅ Warp Terminal
  • ✅ Cursor IDE
  • ✅ ChatGPT web (via clipboard)
  • ✅ Claude web (via clipboard)
  • ✅ Any AI assistant (via clipboard)

4. Context evolution

git log .gptcode/context/shared.md

commit abc123 - "Add Redis caching layer"
commit def456 - "Migrate to microservices"  
commit ghi789 - "Initial monolith architecture"

Your AI’s knowledge evolves with your codebase. Because it’s in your codebase.

Real-World Example

Before: Chaos

Monday morning, Developer A:

AI: What's your architecture?
Dev: We have a monolith with PostgreSQL...
[10 minutes of explanation]

Monday afternoon, Developer B:

AI: What's your architecture?  
Dev: We're migrating to microservices...
[15 minutes of different explanation]

Tuesday, new feature:

AI: What's your architecture?
Dev: Ugh, let me start over...

After: One Source of Truth

.gptcode/context/shared.md (committed to git):

# Architecture

Currently transitioning from monolith to microservices:
- **Monolith** (legacy): Rails app, PostgreSQL
- **Services** (new): 
  - API Gateway (Kong)
  - User Service (Elixir)
  - Payment Service (Node.js)

## Communication
- Services use Redis pub/sub
- Monolith gradually being decomposed

## What to work on
See .gptcode/context/next.md

Every developer, every session:

gptcode context sync  # ← Done

AI knows:

  • Current architecture (monolith + microservices)
  • Communication patterns (Redis pub/sub)
  • Where to find next tasks

Consistent. Always up-to-date. Zero effort.

The Three Context Files

shared.md - The Technical Foundation

Stable, changes slowly (monthly):

  • Architecture
  • Tech stack
  • Patterns & conventions
  • Development setup

next.md - Current Priorities

Changes frequently (weekly):

  • What we’re building now
  • This sprint’s focus
  • Immediate blockers

roadmap.md - The Vision

Changes occasionally (quarterly):

  • Q1/Q2/Q3 goals
  • Future direction
  • Long-term plans

Why separate?

  • AI doesn’t need your Q3 roadmap when fixing a bug
  • “What’s next?” and “How does this work?” are different questions
  • Smaller context = faster AI responses, lower costs

How It Works

1. Single Source of Truth

.gptcode/context/   ← Edit here (ONE place)
  shared.md
  next.md
  roadmap.md

2. Auto-Sync to Integrations

gptcode context sync

# Writes:
WARP.md              ← Warp reads this
.cursor/docs/        ← Cursor reads this

3. Version-Controlled Evolution

git log .gptcode/

commit def456 - "Update: Redis caching added"
commit abc123 - "Update: migrated to microservices"

4. Team Shares via Git

git pull              # Get team's context updates
gptcode context sync  # Update local tools

Use Cases

Large Monorepos

20+ microservices, different patterns per service.

.gptcode/context/shared.md:
## Services
- user-service: Elixir/Phoenix, port 4000
- payment-service: Node.js, port 3000
- notification-service: Go, port 8080
[... 17 more services]

gptcode context sync  # AI knows ALL services

Every session starts with full context. No more “wait, which port does user-service run on?”

Team Onboarding

New developer, day 1:

git clone repo
cd repo
gptcode context show  # ← Full architecture overview in 30 seconds

gptcode context sync  # ← All AI tools have context
# Start coding immediately

No more 2-week ramp-up. Context is in the repo.

Multi-Tool Workflows

Terminal (Warp) + IDE (Cursor) + Design (Claude web):

# Once
gptcode context sync              # Warp + Cursor

# Per session
gptcode context export clipboard  # Claude web

Same context, every tool. Zero copy-paste.

Context Drift Prevention

Architecture changed 3 months ago. Your AI still thinks it’s a monolith.

Solution:

# When you refactor
vi .gptcode/context/shared.md  # Update architecture
git commit -am "Update: split into microservices"

# Everyone gets it
git pull
gptcode context sync

Context stays synchronized with reality.

Best Practices

1. Update Context with Code

git commit -m "feat: Add Redis caching

Code:
- Added Redis client
- Implemented cache-aside pattern

Context:
- Updated .gptcode/context/shared.md with caching strategy
- Removed 'add caching' from .gptcode/context/next.md
"

Context is documentation. It evolves with code.

2. Review Context in PRs

## PR: Add Redis Caching

### Code Changes
- `lib/redis.ex`: Redis client
- `lib/cache.ex`: Cache implementation

### Context Changes
- `shared.md`: Added "Redis Caching" section
- `next.md`: Removed "Implement caching" task

### Reviewers
Please verify context is accurate ✓

3. Keep It Focused

Good:

## Stack
- Backend: Elixir/Phoenix
- Frontend: React + TypeScript
- DB: PostgreSQL

Bad:

## Stack
In 2019, we evaluated Ruby vs Elixir vs Node.js. After
considering various factors including performance, developer
experience, ecosystem maturity, hiring pool, and long-term
maintainability, we decided on Elixir because...
[3 more paragraphs]

Quick reference, not a novel.

Getting Started

Install

# GPTCode CLI includes context management
go install github.com/jadercorrea/gptcode/cmd/gptcode@latest

Initialize

cd ~/your-project
gptcode context init

Fill in Context

vi .gptcode/context/shared.md
vi .gptcode/context/next.md

Sync

gptcode context sync

Commit

git add .gptcode/ WARP.md
git commit -m "Add project context"
git push

That’s it. Your team now shares context.

FAQ

“Isn’t this just documentation?”

Yes, but version-controlled, AI-formatted, auto-synced documentation.

Traditional docs:

  • ❌ Outdated (written once, forgotten)
  • ❌ Not AI-friendly (pages of prose)
  • ❌ Tool-specific (Notion, Confluence, Google Docs)
  • ❌ Separate from code

Context Layer:

  • ✅ Lives with code (.gptcode/ in your repo)
  • ✅ Version-controlled (git tracks changes)
  • ✅ Concise & structured (AI-optimized format)
  • ✅ Universal (works with ANY AI tool)

“Can’t I just use WARP.md?”

You can. But:

  • ❌ Warp-only: Cursor, Claude, ChatGPT don’t read WARP.md
  • ❌ No separation: Mix “what to do next” with “how things work”
  • ❌ No tooling: Manual editing, no sync, no structure

Context Layer:

  • ✅ Universal: One source → all tools
  • ✅ Separated: shared.md, next.md, roadmap.md
  • ✅ Tooling: gptcode context sync, validation, etc.

Think of it as: WARP.md is the compilation target, .gptcode/context/ is the source code.

“My team doesn’t use AI assistants”

They will.

In 2023, “nobody used AI for coding.”
In 2024, Cursor raised $100M, Warp added AI agents.
In 2025, every IDE has AI built-in.

Setting up context now means:

  • ✅ Team is ready when they adopt AI
  • ✅ Onboarding gets easier (context is there)
  • ✅ Better documentation (side benefit)

“What about private/sensitive information?”

Everything is local. No data sent to GPTCode servers.

Context lives in:

  • .gptcode/ in your repo (you control it)
  • Synced to local files (WARP.md, .cursor/docs/)
  • Exported to clipboard (you paste it)

For sensitive projects:

  • Add .gptcode/ to .gitignore (keep context local-only)
  • Or commit public context, keep secrets in separate docs

The Bottom Line

Time is your most valuable asset.

Every minute spent re-explaining your architecture is a minute not spent building.

Universal Context Management gives you:

  • 200 hours/year back (per developer)
  • Consistent AI responses (team-wide)
  • Instant onboarding (new devs/AI tools)
  • Evolutionary documentation (changes with code)

All for running:

gptcode context init

Try It

# Install
go install github.com/jadercorrea/gptcode/cmd/gptcode@latest

# Initialize
cd ~/your-project
gptcode context init

# Fill in context
vi .gptcode/context/shared.md

# Sync
gptcode context sync

# Done

5 minutes to set up.
200 hours/year saved.

Not a bad ROI.

Read the full guide: Context Management Documentation

Questions? Open an issue on GitHub

Share your setup: Join the Discussion