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What This Program Is About

Alexey Grigorev – AI Engineering Buildcamp: From RAG to Agents is a hands-on, production-focused AI engineering course designed to take you from basic LLM knowledge to building real-world AI agents and applications.

Unlike beginner tutorials, it emphasizes:

• End-to-end systems
• Engineering rigor (testing, monitoring)
• Deployable AI products

The core goal:
Build, evaluate, and ship a production-ready AI assistant or agent system.

Core Curriculum Breakdown

1. Foundations: LLMs & RAG

• Learn Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)
• Build assistants that use real data (docs, GitHub, YouTube, etc.)
• Create structured pipelines with APIs

Output: A working RAG-based AI assistant

2. Agentic AI Systems

• Add decision-making + tool usage
• Use frameworks like:
  • PydanticAI
  • OpenAI Agents SDK
• Integrate tools via MCP (Model Context Protocol)

Output: AI agents that can take actions (not just chat)

3. Testing & Evaluation

• Unit testing for AI systems
• Use LLMs as “judges”
• Compare prompts, models, and retrieval strategies

Focus: Data-driven optimization instead of guesswork

4. Monitoring & Guardrails

• Observability tools:
  • Grafana
  • OpenTelemetry
  • Evidently
• Track:
  • Costs
  • Token usage
  • Errors

Output: Production-ready reliability & safety

5. Real Use Cases & Projects

You’ll build multiple systems, such as:

• FAQ assistant
• YouTube Q&A system
• AI coding agent
• Deep research agent
• Code reviewer

Total: 8+ hands-on projects

6. Capstone Project

• Build a complete AI application from scratch
• Use your own data
• Fully tested + monitored

Output: Portfolio-ready project for jobs or clients

Who It’s For

Best suited for:

• Software engineers
• Data scientists / ML engineers
• Developers stuck at “tutorial level”

Not ideal for:

• Beginners with no coding experience

Prerequisites:

• Python, Git, Docker, CLI
• API usage (OpenAI or alternatives)

Key Value Proposition

What makes it stand out:

• Project-first learning (not theory-heavy)
• Focus on production systems (LLMOps mindset)
• Covers full lifecycle:
  • Build → Evaluate → Monitor → Deploy

A strong emphasis is placed on engineering discipline, not just prompt engineering.

Outcomes

By the end, you will:

• Build AI assistants using real-world data
• Create tool-using agents
• Implement testing + evaluation pipelines
• Deploy monitored, production-grade AI systems
• Ship a portfolio-ready capstone project

Pros & Cons

Pros

• Highly practical and industry-relevant
• Covers modern AI stack (RAG → Agents → LLMOps)
• Strong portfolio output
• Taught by experienced practitioner

Cons

• Expensive (~$1.8K official price)
• Requires solid coding background
• Time-intensive (5–10+ hrs/week + projects)

Final Verdict

This is not a beginner AI course—it’s closer to an AI engineering bootcamp for professionals.

Best described as: A bridge from “playing with LLMs” → “building production AI systems.”

If your goal is:

• Getting into AI engineering roles
• Building AI SaaS / agents
• Moving beyond ChatGPT-style apps

Then this course is highly relevant and practical.

See More: Matt Gray – FounderOS

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