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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)
- Unit testing for AI systems
- Use LLMs as βjudgesβ
- 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
Youβll 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 Itβs For
Best suited for:
- Software engineers
- Data scientists / ML engineers
- Developers stuck at βtutorial levelβ
Not ideal for:
- Beginners with no coding experience
- 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 β Evaluate β Monitor β 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 β Agents β LLMOps)
- Strong portfolio output
- Taught by experienced practitioner
Cons
- Expensive (~$1.8K official price)
- Requires solid coding background
- Time-intensive (5β10+ hrs/week + projects)
Final Verdict
This is not a beginner AI courseβitβs closer to an AI engineering bootcamp for professionals.
Best described as: A bridge from βplaying with LLMsβ β βbuilding production AI systems.β
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 β FounderOS
Alexey Grigorev – AI Engineering Buildcamp: From RAG to Agents
Name of course: Alexey Grigorev – AI Engineering Buildcamp: From RAG to Agents
Delivery Method:Β Instant DownloadΒ (Mega)
Contact for more details:Β isco.coursebetter@gmail.com




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