From Zero to Deploy
Build your own ChatBot with RAG in 4 weeks. One session per week in February/2026, with ongoing mentor support throughout the program.
Gabriel Chaves & Leandro Barbosa
AI Engineers | LLMs, RAG & Distributed Systems
Why this mentorship is different
Most RAG tutorials only show the basics and skip the hard parts. This mentorship takes you from absolute zero to a system running in production.
Tutorials that don't explain the 'why' behind architectural choices
Courses that focus on code but ignore LLM fundamentals
Difficulty connecting theory with practical implementation
Lack of guidance for deploying to real environments
This mentorship covers everything: OpenAI fundamentals, RAG architecture, hands-on implementation and complete deploy.
4 weeks with weekly sessions + ongoing mentor support between sessions.
What you will learn
OpenAI & LLM Fundamentals
What is OpenAI, available models, Python SDK setup and essential parameters (top_k, temperature, etc.)
Naive RAG Architecture
Understand what RAG is, when to use it, LLM limitations and the comparison RAG vs Fine-Tuning vs Prompting
Complete Data Pipeline
Ingestion, data preparation, chunking strategies and embedding generation
Retrieval & Vector Database
Semantic search, result ranking and LLM integration for response generation
ChatBot Construction
Implement your own ChatBot using your data, with full RAG pipeline working
Production Deploy
Front-end on Cloudflare Pages, backend on Render and deploy best practices
Mentorship Format
4 weeks in February/2026. One session per week + ongoing mentor support between sessions.
4 live sessions
One per week in February/2026
2h per session
Time for theory and practice
Ongoing support
Ask questions between sessions
Small cohorts
Guaranteed individual attention
Guided hands-on
You build alongside the mentors
Source code included
Complete repository for reference
4-Week Track
Week 1: Leveling
OpenAI, models, Python SDK and chat-completions
Week 2: Why RAG
Fundamentals, LLM limitations and Naive RAG architecture
Week 3: Build the Stack
Build the ChatBot with your data - complete pipeline
Week 4: Deploy
Front-end on CF Pages, backend on Render
Your Mentors
Gabriel Chaves
AI Engineer | LLMs, RAG & Agentic Systems
Software and AI Engineer with strong experience in intelligent systems and agentic architectures. Designed conversational agents, NLP pipelines and model routing systems. Experience with LLMs, RAG, embeddings, vector databases, LangGraph, CrewAI and ADK.
Focus
LLMs, RAG, Multi-Agent Systems, Applied NLP
Leandro Barbosa
CTO & Lead Engineer | AI & Distributed Systems
Lead engineer and CTO with extensive experience in large-scale distributed systems and AI infrastructure. Architected the largest Video CDN in South America (Globo). Focus on real-world constraints, performance optimization and operational excellence.
Focus
Distributed Systems, Production RAG, Cloud & DevOps
Who this is for
Ideal participants
Developers who want to enter the world of AI/LLMs
Software engineers wanting to build products with AI
Professionals looking to implement RAG in their projects
Tech leads evaluating architectures with LLMs
Any dev with Python background wanting to evolve into AI Engineering
Not suitable for
- • Complete beginners to programming
- • Those looking for no-code or low-code solutions
- • People without basic Python familiarity
- • Anyone expecting results without committing to all 4 weeks
Prerequisites: Basic Python knowledge and familiarity with APIs. Basic understanding of Git & GitHub. No prior experience with LLMs or AI required.
Frequently asked questions
Ready to build your ChatBot with RAG?
4 weeks in February/2026 with ongoing support. Limited seats.
Applications will be reviewed on a rolling basis. You'll hear from us within 48 hours.