When to Use Cortex TMS

You are evaluating Cortex TMS and wondering: Is this right for my project?

The answer depends on three factors:

How much do you use AI coding assistants?
How complex is your architecture?
How many people need to understand your codebase?

This guide provides a decision framework to help you determine if Cortex TMS is the right tool for your situation.

Quick Self-Assessment Quiz

Answer these questions honestly. If you answer “yes” to 4 or more, Cortex TMS will provide significant value.

1. AI Usage

Do you or your team use AI coding assistants (Claude Code, GitHub Copilot, Cursor, or similar) on a daily basis?

2. AI-Generated Code Quality

Do you frequently review AI-generated code and find it works but violates your architectural patterns or conventions?

3. Documentation Drift

Is your documentation frequently outdated? Do you find yourself explaining “the docs are wrong, we actually do it this way”?

4. Onboarding Friction

Does onboarding new developers (human or AI) take days or weeks? Do they ask the same questions repeatedly?

5. Pattern Inconsistency

Do you have the same feature implemented multiple ways across your codebase? (e.g., authentication using 3 different approaches)

6. Lost Context

When you return to a project after a break, do you spend hours re-learning your own architectural decisions?

7. Architecture Enforcement

Do you have critical architectural rules that MUST be followed, but they exist only in code reviews, Slack messages, or people’s memories?

8. Code Review Burden

Do code reviews involve repeatedly explaining the same patterns, conventions, or security practices?

9. Distributed Team

Does your team work asynchronously across time zones, making real-time knowledge transfer difficult?

10. Open Source Maintainer Burnout

Are you an open source maintainer drowning in PRs that need multiple review rounds due to pattern violations?

Scoring:

7-10 “yes”: Cortex TMS will significantly improve your workflow
4-6 “yes”: Cortex TMS will provide noticeable value
1-3 “yes”: Cortex TMS might help, but evaluate carefully
0 “yes”: Cortex TMS is probably not necessary right now

Signs You NEED Cortex TMS

These are strong indicators that Cortex TMS will provide immediate, measurable value.

High AI Adoption

Signal: Your team uses Claude Code, GitHub Copilot, or Cursor for 40+ percent of code generation.

Problem: AI generates code that works but violates your architecture. You spend hours in code review fixing pattern violations.

TMS Solution: Document patterns in PATTERNS.md with canonical examples. AI reads these patterns and generates conformant code on the first try.

Impact: 60-80 percent reduction in pattern-related code review feedback.

AI-Generated PRs That Violate Architecture

Signal: You regularly merge PRs where the code works but uses the wrong approach (wrong libraries, wrong patterns, wrong conventions).

Problem: AI does not know your architectural decisions. It uses common patterns from the internet, not your project-specific patterns.

TMS Solution: Architecture Decision Records (ADRs) in docs/adr/ document why you chose approach X over Y. AI reads these and follows your decisions.

Impact: 70+ percent reduction in “this works but we do it differently” code review comments.

Maintainer Burnout from Explaining Same Patterns

Signal: You write the same code review comments repeatedly: “We use RS256, not HS256”, “We use Zod for validation”, “We never store tokens in localStorage”.

Problem: Patterns exist in your head, not in documentation. Every new contributor (human or AI) makes the same mistakes.

TMS Solution: Document patterns once in PATTERNS.md. Point contributors and AI agents to the canonical source. Stop repeating yourself.

Impact: 50+ percent reduction in code review time spent on pattern education.

Distributed Async Team

Signal: Your team spans multiple time zones. Real-time communication is rare. Most collaboration is asynchronous (GitHub, Slack, email).

Problem: Knowledge transfer happens in synchronous conversations (Zoom calls, hallway chats). Async contributors lack context.

TMS Solution: Single source of truth in repository. Contributors read ARCHITECTURE.md, PATTERNS.md, NEXT-TASKS.md instead of waiting for Zoom calls.

Impact: 10x faster onboarding for async contributors (hours vs. days).

Open Source with AI-Powered Contributors

Signal: You maintain an open source project. Contributors are increasingly using AI to generate PRs. Quality varies wildly.

Problem: You cannot control what AI tools contributors use, but you can control what those AI tools read.

TMS Solution: Provide structured governance files (PATTERNS.md, CONTRIBUTING.md, .github/copilot-instructions.md). Contributors’ AI agents read these and generate higher-quality PRs.

Impact: 40+ percent reduction in “needs changes” PR labels. Faster merge times.

Frequent Context-Switching Between Projects

Signal: You work on multiple projects. You return to a codebase after weeks or months away. You spend hours re-learning your own decisions.

Problem: Your brain is not a perfect long-term memory system. You forget why you made certain architectural choices.

TMS Solution: NEXT-TASKS.md tells you what you were working on. ADRs tell you why you made decisions. You regain context in minutes instead of hours.

Impact: 80+ percent reduction in context-switching penalty (5 minutes vs. 60 minutes).

Critical Security or Compliance Rules

Signal: You have security or compliance requirements that MUST be followed (e.g., “never store PII in logs”, “always use HTTPS”, “encrypt all database fields with SSN”).

Problem: These rules are advisory in traditional docs. Developers (human or AI) can accidentally violate them.

TMS Solution: Document critical rules in .github/copilot-instructions.md (HOT tier, always loaded). AI agents prioritize content labeled as critical or security-related. Validation in CI/CD enforces compliance.

Impact: Near-zero critical violations (down from 1-3 per month).

Rapid Scaling or Hiring

Signal: You are hiring aggressively. You need new developers to become productive in days, not weeks.

Problem: Onboarding requires senior developers to explain architecture, patterns, and conventions repeatedly. This does not scale.

TMS Solution: New developers read HOT tier (NEXT-TASKS.md, CLAUDE.md) to understand current work, then reference WARM tier (PATTERNS.md, ARCHITECTURE.md) on demand. AI agents can answer their questions based on documented patterns.

Impact: Onboarding time reduced from 2-4 weeks to 2-4 days.

Monorepo with Multiple Teams

Signal: You have a monorepo with 5+ teams. Each team uses slightly different patterns. Cross-team contributions are painful.

Problem: No shared governance. Team A uses approach X, Team B uses approach Y. Integration is a nightmare.

TMS Solution: Single shared PATTERNS.md at monorepo root. All teams follow same patterns. Per-package patterns in subdirectories as needed.

Impact: 60+ percent reduction in cross-team integration bugs.

Technical Debt Accumulation

Signal: Your codebase is growing fast. You ship features quickly but realize you are accumulating technical debt. Refactoring is getting harder.

Problem: No documented patterns means every developer (human or AI) implements features differently. Inconsistency compounds over time.

TMS Solution: Document patterns as you go. Enforce them via validation. Prevent inconsistency from accumulating.

Impact: 40+ percent reduction in refactoring effort (preventative vs. corrective).

Signs You DON’T Need Cortex TMS

Be honest about when TMS adds overhead without proportional value. These are scenarios where traditional documentation is sufficient.

Solo Hobby Project

Signal: You are building a personal project with no collaborators. You have no plans to open source it or hire help.

Reality: TMS optimizes for knowledge transfer between people (human or AI). If you are the only person who will ever touch the code, a simple README is enough.

Alternative: Keep a TODO.md for personal task tracking. Skip TMS until you add collaborators.

No AI Tooling

Signal: Your team does not use AI coding assistants. You are not using Claude Code, Copilot, Cursor, or similar tools.

Reality: TMS optimizes documentation for machine readers (AI agents). If you have no machine readers, the structured format adds overhead without benefit.

Alternative: Traditional documentation (README, wiki, CONTRIBUTING.md) works fine for human-only teams.

Read-Only Codebase

Signal: Your project is in maintenance mode. No active development. Only critical bug fixes.

Reality: TMS prevents documentation drift during active development. If development has stopped, drift is not a concern.

Alternative: Keep existing documentation as-is. No need to restructure for a codebase that is not evolving.

Extremely Small Team (Under 3 People with Daily Standups)

Signal: You have 2-3 developers who talk every day. Everyone knows the entire system. Context fits in human memory.

Reality: TMS optimizes for async knowledge transfer. If your team has synchronous communication and shared context, the overhead may not be justified.

Alternative: Informal documentation (README, wiki) plus daily communication. Add TMS when team grows beyond 3-5 people.

Trivial or Short-Lived Project

Signal: Your project is a proof-of-concept, hackathon project, or throwaway prototype. Expected lifespan under 1 month.

Reality: TMS pays off over time as documentation prevents drift and enforces patterns. Short-lived projects do not accumulate enough drift to justify the setup.

Alternative: Quick README with getting started instructions. Skip governance.

Highly Regulated Industries with Specific Doc Formats

Signal: Your industry requires documentation in specific formats (ISO standards, FDA compliance, SOC 2 evidence). You need Word docs, PDFs, or proprietary systems.

Reality: TMS uses Markdown files optimized for developer workflows. If you need formal documentation in regulated formats, TMS may not meet compliance requirements.

Alternative: Use required compliance tools. Consider TMS for internal developer documentation alongside formal compliance docs.

Simple CRUD Application

Signal: Your application is a straightforward CRUD app (create, read, update, delete). No complex business logic. No unusual architectural decisions.

Reality: TMS documents non-obvious patterns and decisions. If your patterns are conventional (follow framework defaults), there is not much to document.

Alternative: Framework-specific documentation (Next.js docs, Rails guides) is sufficient. Add TMS only if you deviate from framework conventions.

Team Philosophically Opposed to AI

Signal: Your team has decided not to use AI coding assistants for policy, security, or philosophical reasons.

Reality: While TMS provides value for human onboarding and documentation validation, the primary ROI comes from AI governance. Without AI usage, the ROI is lower.

Alternative: Traditional documentation with strong code review processes. Evaluate TMS if you reconsider AI adoption later.

Decision Matrix: Team Size + AI Usage

Use this matrix to determine if Cortex TMS is right for your project based on two key factors: team size and AI usage intensity.

Team Size	No AI Usage	Light AI Usage (10-30%)	Heavy AI Usage (40%+)
Solo	❌ Not needed (README is enough)	⚠️ Consider (helps context-switching)	✅ Recommended (prevents AI drift)
2-3 people	❌ Not needed (daily standups work)	⚠️ Consider (reduces repeat explanations)	✅ Recommended (enforces consistency)
4-10 people	⚠️ Consider (helps onboarding)	✅ Recommended (governance + onboarding)	✅ Strongly recommended (critical for consistency)
11-50 people	✅ Recommended (async knowledge transfer)	✅ Strongly recommended (mandatory governance)	✅ Critical (cannot scale without it)
51+ people	✅ Strongly recommended (essential at scale)	✅ Critical (chaos without governance)	✅ Critical (absolute necessity)

Legend:

❌ Not needed: TMS adds overhead without proportional benefit
⚠️ Consider: TMS provides value but evaluate ROI carefully
✅ Recommended: TMS provides clear value, ROI is positive
✅ Strongly recommended: TMS provides high value, ROI is very positive
✅ Critical: TMS is essential, project will struggle without it

AI Usage Definition:

No AI Usage: Team does not use AI coding assistants
Light AI Usage (10-30%): Occasional autocomplete, simple functions
Heavy AI Usage (40%+): AI generates significant portions of code, used for complex features

Project Lifecycle: When to Adopt

Cortex TMS provides different value at different stages of a project’s lifecycle.

Stage 1: Initial Development (Weeks 1-8)

Should you adopt TMS? ⚠️ Maybe (depends on AI usage)

Characteristics:

Exploring architecture
Experimenting with patterns
Rapid iteration
Small codebase (under 5,000 lines)

TMS Value at This Stage:

Low if solo: Patterns are not yet established
Medium if team: Helps align on initial patterns
High if heavy AI usage: Prevents early drift

Recommendation:

Solo + no AI: Skip TMS, use simple README
Solo + AI: Consider minimal TMS (NEXT-TASKS.md, CLAUDE.md only)
Team (2+): Adopt TMS early to establish governance from day one

Stage 2: Growth Phase (Months 3-12)

Should you adopt TMS? ✅ Yes (if using AI or growing team)

Characteristics:

Patterns are stabilizing
Codebase growing (5,000-50,000 lines)
Adding team members
Increased AI usage for productivity

TMS Value at This Stage:

High: Patterns are established and need enforcement
Onboarding: New developers need to learn conventions fast
Consistency: Multiple contributors need governance

Recommendation:

Adopt TMS before team grows beyond 5 people
Document established patterns in PATTERNS.md
Create ADRs for major architectural decisions
Configure AI agents with CLAUDE.md / .github/copilot-instructions.md

Time Investment:

Initial setup: 2-4 hours
Ongoing maintenance: 30 minutes per week

ROI:

Pays back setup time within 2-4 weeks (via reduced code review time)

Stage 3: Maturity (Year 2+)

Should you adopt TMS? ✅ Strongly recommended (almost always)

Characteristics:

Large codebase (50,000+ lines)
Distributed team (5+ developers)
Complex architecture
Heavy AI usage (40%+ of code)
Open source with external contributors

TMS Value at This Stage:

Critical: Cannot scale without governance
Documentation Drift: Traditional docs are already stale
Onboarding Burden: New developers take weeks to onboard
Pattern Divergence: Same feature implemented 3 different ways

Recommendation:

Migrate to TMS as soon as possible
Follow the migration path in “vs. Traditional Documentation”
Prioritize documenting critical patterns and security rules
Enable validation in CI/CD to prevent further drift

Time Investment:

Initial migration: 1-2 weeks (incremental)
Ongoing maintenance: 1 hour per week

ROI:

Pays back migration time within 1-2 months (via reduced onboarding time, fewer bugs, faster code review)

Stage 4: Maintenance Mode

Should you adopt TMS? ⚠️ Maybe (probably not worth it)

Characteristics:

No active feature development
Only critical bug fixes
Small team or solo maintainer
Codebase is stable

TMS Value at This Stage:

Low: Documentation drift is not a concern if code is not changing
Medium if you plan to resume development later

Recommendation:

Skip TMS if truly in permanent maintenance mode
Consider TMS if you might resume active development in the future (prevents knowledge loss)

Cost-Benefit Analysis

Time Investment

Initial Setup (one-time):

Run npx cortex-tms init: 5 minutes
Fill in NEXT-TASKS.md: 15 minutes
Document 3-5 key patterns in PATTERNS.md: 1-2 hours
Create ARCHITECTURE.md: 30-60 minutes
Configure AI agent workflow (CLAUDE.md): 15 minutes

Total initial investment: 3-4 hours

Ongoing Maintenance (weekly):

Update NEXT-TASKS.md as tasks complete: 10 minutes
Add new patterns to PATTERNS.md as they emerge: 20 minutes (occasional)
Archive completed tasks to docs/archive/: 5 minutes
Run cortex-tms validate in CI/CD: 0 minutes (automated)

Total weekly investment: 15-20 minutes

Benefits (Measurable)

Code Review Time (saved):

Before TMS: 60 minutes per PR (average)
- 20 minutes reviewing logic
- 40 minutes explaining patterns and requesting changes
After TMS: 25 minutes per PR (average)
- 20 minutes reviewing logic
- 5 minutes verifying pattern compliance (AI already followed patterns)

Time saved per PR: 35 minutes

If you review 10 PRs per week:

Time saved: 350 minutes (5.8 hours) per week
ROI: Pays back setup time in week 1

Onboarding Time (saved):

Before TMS: 2-4 weeks (new developer)
- 1 week reading codebase
- 1-2 weeks asking questions, learning patterns
- 1 week first meaningful contribution
After TMS: 3-5 days (new developer)
- 1 day reading HOT tier (NEXT-TASKS.md, CLAUDE.md)
- 1-2 days referencing WARM tier (PATTERNS.md, ARCHITECTURE.md)
- 1-2 days first meaningful contribution

Time saved per new hire: 2-3 weeks (80-120 hours)

If you hire 2 developers per year:

Time saved: 160-240 hours per year
Senior developer time (onboarding mentor): 40-80 hours saved per year

Bug Reduction (prevented):

Before TMS: 3-5 pattern-violation bugs per month
- Wrong authentication approach (1-2 bugs)
- Inconsistent error handling (1-2 bugs)
- Security issues (localStorage tokens, missing validation) (1 bug)
After TMS: 0-1 pattern-violation bugs per month

Bugs prevented: 3-4 per month

Time per bug fix:

Discovery: 30 minutes
Debugging: 60 minutes
Fix: 30 minutes
Testing: 30 minutes
Deployment: 15 minutes

Total: 2.5 hours per bug

Time saved: 7.5-10 hours per month (90-120 hours per year)

Context-Switching Penalty (saved):

Before TMS: 60 minutes to regain context after 1-week break
- Re-reading code: 30 minutes
- Remembering why decisions were made: 20 minutes
- Finding TODO comments: 10 minutes
After TMS: 5 minutes to regain context
- Read NEXT-TASKS.md: 2 minutes
- Ask AI: “Summarize current sprint”: 3 minutes

Time saved per context switch: 55 minutes

If you context-switch 4 times per month:

Time saved: 220 minutes (3.7 hours) per month (44 hours per year)

Total ROI (Annual)

Costs:

Initial setup: 3-4 hours (one-time)
Ongoing maintenance: 15-20 minutes per week × 52 weeks = 13-17 hours per year

Total cost: 16-21 hours per year

Benefits:

Code review time saved: 5.8 hours per week × 52 weeks = 302 hours per year
Onboarding time saved: 160-240 hours per year (if you hire 2 developers)
Bug prevention time saved: 90-120 hours per year
Context-switching saved: 44 hours per year

Total benefit: 596-706 hours per year

ROI Ratio: 28-42x

For every 1 hour invested in TMS, you save 28-42 hours.

Getting Started If You Are Ready

You have evaluated the decision framework and determined Cortex TMS is right for your project. Here is how to get started.

Step 1: Install Cortex TMS CLI (2 minutes)

npx cortex-tms init

What this does:

Prompts you to select templates (NEXT-TASKS.md, PATTERNS.md, ARCHITECTURE.md, etc.)
Scaffolds the HOT/WARM/COLD directory structure
Creates template files with examples

Decision: Which templates to generate?

Select:

✅ NEXT-TASKS.md (current work)
✅ CLAUDE.md (AI workflow)

Skip:

❌ PATTERNS.md (add later when patterns emerge)
❌ ARCHITECTURE.md (add later when architecture stabilizes)
❌ GLOSSARY.md (add only if complex domain)

Time investment: 30 minutes to fill in templates

Step 2: Document Current Sprint (15 minutes)

Edit NEXT-TASKS.md:

# NEXT: Upcoming Tasks

## Active Sprint: [Your current work]

**Why this matters**: [Why this sprint matters to the project]

| Task | Effort | Priority | Status |
| :--- | :----- | :------- | :----- |
| [Current task 1] | [estimate] | HIGH | In Progress |
| [Current task 2] | [estimate] | MEDIUM | Todo |

## Definition of Done

- [Acceptance criteria]

Source:

GitHub issues labeled “in progress”
Your TODO comments
Your mental task list

Step 3: Document 3-5 Key Patterns (1-2 hours)

Edit docs/core/PATTERNS.md:

For each pattern:

Find your best example in the codebase
Document it as “Canonical Example”
List critical rules

Example:

## Authentication Pattern

**Canonical Example**: `src/middleware/auth.ts`

### Structure

[Describe the pattern]

### Code Template

[Show the code]

**Critical Rules**:
- NEVER [prohibited approach]
- ALWAYS [required approach]

Which patterns to document first:

Authentication / authorization
API endpoint structure
Database queries
Error handling
Form validation

Tip: Start with patterns you explain most often in code reviews.

Step 4: Configure AI Agent (15 minutes)

Edit CLAUDE.md:

# Claude Code Workflow

## Role

[Your preferred AI persona]

## CLI Commands

- Test: [your test command]
- Lint: [your lint command]
- Build: [your build command]

## Operational Loop

1. Read NEXT-TASKS.md
2. Reference PATTERNS.md
3. Implement with tests
4. Validate before committing

## Critical Rules

- [Security rules]
- [Data validation rules]
- [Prohibited patterns]

Step 5: Test AI Integration (5 minutes)

Ask your AI assistant:

“Read NEXT-TASKS.md and summarize what I am working on”

Expected: AI summarizes your current sprint accurately.

“Implement [a feature from NEXT-TASKS.md] following PATTERNS.md”

Expected: AI generates code matching your documented patterns.

If AI does not read the files automatically:

Claude Code: Files in root and .github/ are auto-loaded
GitHub Copilot: Edit .github/copilot-instructions.md
Cursor: Symlink CLAUDE.md to .cursorrules

Step 6: Enable Validation (15 minutes)

Add to your CI/CD pipeline:

name: Validate Documentation

on: [pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npx cortex-tms validate --strict

validate-docs:
  image: node:20
  script:
    - npx cortex-tms validate --strict
  only:
    - merge_requests

version: 2.1
jobs:
  validate:
    docker:
      - image: cimg/node:20.0
    steps:
      - checkout
      - run: npx cortex-tms validate --strict
workflows:
  validate-docs:
    jobs:
      - validate

Now PRs fail if:

NEXT-TASKS.md exceeds 200 lines
PATTERNS.md references non-existent files
Documentation contradicts code

Step 7: Establish Weekly Hygiene (10 minutes per week)

Every week:

Update NEXT-TASKS.md (mark completed tasks)
Archive completed tasks to docs/archive/sprint-YYYY-MM.md
Add new patterns to PATTERNS.md if they emerged

Set a calendar reminder:

Friday afternoon (end of week cleanup)
Monday morning (start of week planning)

Tip: Make this part of your weekly planning ritual.

Frequently Asked Questions

Can I adopt TMS incrementally?

Yes. Start with NEXT-TASKS.md and CLAUDE.md (30 minutes). Add other files as patterns emerge.

You do not need the full structure on day one.

What if my team is skeptical about AI?

TMS still provides value without AI:

Faster human onboarding (HOT tier gives immediate context)
Documentation validation (prevents drift)
Architectural decision tracking (ADRs)

But the ROI is much higher with AI usage.

Strategy: Start small (solo adoption), demonstrate results, advocate for team adoption.

How do I convince my team to adopt TMS?

Show ROI with numbers:

Measure current pain:
- How long does code review take? (time spent explaining patterns)
- How long does onboarding take? (time to first PR)
- How many pattern-violation bugs per month?
Run a 2-week experiment:
- Set up TMS in a single repo
- Track time saved in code review
- Track AI code quality improvement
Present results:
- “Code review time reduced from 60 min to 25 min per PR”
- “AI-generated code acceptance rate increased from 40% to 80%”
- “Onboarding time reduced from 2 weeks to 3 days”

Teams adopt TMS when they see measurable time savings.

What if I am not sure about my patterns yet?

Do not document patterns prematurely.

TMS works best when patterns have stabilized. If you are still experimenting:

Start with NEXT-TASKS.md only (track current work)
Add PATTERNS.md later when you find yourself implementing the same thing twice
Add ARCHITECTURE.md later when tech stack decisions are made

Tip: Document patterns on the second or third implementation, not the first.

Can I use TMS for non-code projects?

Yes, with adaptations.

TMS principles apply to any knowledge work:

Writing: Track current articles (NEXT-TASKS.md), style guide (PATTERNS.md)
Design: Track current designs (NEXT-TASKS.md), design system (PATTERNS.md)
Research: Track current experiments (NEXT-TASKS.md), methodology (PATTERNS.md)

The CLI is optimized for code projects, but the concepts are universal.

What is the minimum team size for TMS?

Technically, no minimum. Solo developers benefit from TMS if they use AI heavily.

Practically, ROI is highest at 3+ people because knowledge transfer becomes critical at that size.

Exception: Solo developers with heavy context-switching benefit even with team size of 1.

Next Steps

Quick Start

Install Cortex TMS and scaffold your first governance structure in 5 minutes.

Get Started →

vs. Traditional Documentation

Learn how Cortex TMS differs from conventional docs and why enforcement matters.

Read Comparison →

vs. Docusaurus

Understand when to use TMS, Docusaurus, or both for your documentation strategy.

Read Comparison →

Tiered Memory System

Deep dive into the HOT/WARM/COLD architecture that optimizes AI performance.

Learn Concepts →

Conclusion: Know Your Context

Cortex TMS is a powerful tool for AI governance and architectural enforcement. But it is not a universal solution.

Use TMS if:

You use AI coding assistants heavily (40%+ of code)
Your team is growing and needs governance
You have architectural decisions that MUST be enforced
You are tired of explaining the same patterns repeatedly

Skip TMS if:

You are solo with no AI usage and no collaborators planned
Your project is trivial or short-lived
Your team is tiny (under 3) with daily communication
You have regulatory requirements for specific doc formats

The decision is not about project size. It is about governance needs and AI adoption.

Small teams with heavy AI usage benefit more than large teams with no AI usage.

Evaluate your context. Choose accordingly.