Skip to content

🤖 Explain with AI

Getting Started with 4Geeks AI Studio: From Console to First Deploy

Overview

4Geeks AI Studio gives you access to an AI-augmented development team powered by the 4Geeks AI Factory — a proprietary infrastructure that orchestrates the world’s most powerful LLMs (Claude 4.5, GPT-5, Gemini 3 Pro, and DeepSeek) to write, test, and refactor code up to 5x faster than a standard developer.

In this tutorial, you will:

  • Create your 4Geeks AI Studio account
  • Navigate the console dashboard
  • Configure your first project and set token limits
  • Submit your first AI task
  • Monitor results in real-time

Prerequisites

  • A 4Geeks account (sign up at console.4geeks.io)
  • A code repository (GitHub, GitLab, or Bitbucket)
  • Basic understanding of software development workflows

Step 1: Create Your Account

  1. Go to console.4geeks.io/ai-studio
  2. Click “Get Started”
  3. Fill in your company information and select your plan:
  4. AI Solo ($1,950/mo) — Ideal for MVP development
  5. AI Starter ($3,450/mo) — Ideal for scaling products
  6. AI Pro ($5,900/mo) — Ideal for enterprise workloads
  7. Complete the onboarding process and verify your email

Step 2: Explore the Console Dashboard

Once logged in, you’ll see the AI Studio dashboard with the following sections:

Section Description
Projects List of all your active AI-augmented projects
Token Usage Real-time consumption meter with hard-limit controls
AI Tasks Queue of submitted tasks and their status
Team Your assigned Senior Architect and project managers
Billing Subscription details, invoices, and token spend history

Step 3: Create Your First Project

  1. Click “New Project” on the dashboard
  2. Enter your project details:
  3. Project Name: A descriptive name for your initiative
  4. Repository URL: Link to your GitHub/GitLab/Bitbucket repo
  5. Tech Stack: Select the primary languages and frameworks
  6. Description: Brief overview of what you want to build
  7. Click “Create Project”

Your Senior Architect will be assigned within 24 hours and will begin mapping your codebase for Smart Context Injection.

Step 4: Configure Token Limits

One of AI Studio’s key features is Hard-Limit Protection — you control exactly how much you spend on AI compute.

  1. Navigate to your project settings
  2. Go to “Token Configuration”
  3. Set your monthly token cap (e.g., $150/mo)
  4. Choose your alert preference:
  5. Pause — AI tasks stop when the limit is reached
  6. Alert — You receive a notification but tasks continue
  7. Click “Save”

Tip: Start with a conservative limit and increase it as you understand your project’s token consumption patterns.

Step 5: Submit Your First AI Task

An AI Task is an atomic, testable unit of work — such as one Pull Request, a specific API endpoint, a UI component, or a bug fix. It is not an entire project epic.

  1. Go to your project’s “AI Tasks” tab
  2. Click “New Task”
  3. Fill in the task details:
  4. Title: Clear, specific description (e.g., “Create user authentication endpoint”)
  5. Type: Feature, Bug Fix, Refactor, or Test
  6. Priority: Low, Medium, High, or Critical
  7. Description: Detailed requirements, acceptance criteria, and any relevant context
  8. Attach any relevant files, designs, or API specifications
  9. Click “Submit Task”

Step 6: Monitor Progress

Your Senior Architect will:

  1. Review the task and write optimized prompts
  2. Inject the right project context into the AI Factory
  3. Generate code using the best-suited LLM
  4. Run automated QA and security guardrails
  5. Review and approve the output
  6. Submit a Pull Request to your repository

You can track progress in real-time from the AI Tasks dashboard, which shows:

  • Task status (Pending, In Progress, Under Review, Completed)
  • Token consumption per task
  • Time estimates and actual completion time
  • PR links and code review comments

Step 7: Review and Merge

When a task is completed:

  1. You’ll receive a notification (email or Slack, based on your preferences)
  2. Review the Pull Request in your repository
  3. The code has already passed through:
  4. Automated vulnerability scans
  5. AI-driven unit tests
  6. Human review by your Senior Architect
  7. Add any additional comments if needed
  8. Merge the PR when satisfied

Best Practices

Writing Effective AI Tasks

  • Be specific: Instead of “Build the backend,” write “Create REST endpoint POST /api/users with email validation and password hashing”
  • Include acceptance criteria: Define what “done” looks like
  • Provide context: Link to designs, API docs, or related PRs
  • Start small: Begin with well-scoped tasks to calibrate expectations

Managing Token Budget

  • Monitor the Real-Time Token Audit dashboard daily
  • Use the LiteLLM-powered analytics to identify which features consume the most tokens
  • Set project-level caps to prevent unexpected charges
  • Review the monthly token report to optimize future task submissions

Working with Your Senior Architect

  • Schedule a weekly sync to align on priorities
  • Provide feedback on code quality and architectural decisions
  • Share your coding standards and naming conventions early
  • Use the integrated chat for quick questions and clarifications

What’s Next?

Need Help?

Still questions? Ask the community.