AI Engineering Buildcamp: From RAG to Agents

AI Engineering Buildcamp: From RAG to Agents

Original price was: $249.99.Current price is: $47.95.

• Format: Digital Download

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Build your own Agentic AI Assistant

This course takes you from core concepts to production-grade AI systems through hands-on, project-focused modules.

  1. LLMs & RAG
    Learn Large Language Models and Retrieval-Augmented Generation. Build conversational agents using the OpenAI SDK and create data-processing pipelines.
    Outcome: A RAG pipeline with real data.
  2. Agentic Flows + MCP
    Add agentic behavior with function calling, using libraries like PydanticAI, and Agents SDK. Expose tools via MCP.
    Outcome: A capable, tool-using agents.
  3. Testing & Evaluation
    Improve through testing and offline evaluation. Use LLMs as judges to compare approaches. Learn tools like Evidently and LangWatch.
    Outcome: A thoroughly tested and evaluated assistant.
  4. Monitoring & Guardrails
    Use Grafana, Pydantic Logfire and OpenTelemetry for observability and safety.
    Outcome: Real-time monitoring.
  5. Use Cases
    Create two agents: a website generator and a code reviewer. Learn about other use cases.
  6. Capstone
    Build an end-to-end AI application with your data.
    Outcome: A portfolio-ready project.

Hackathon: Collaborate on real-world problems.

By the end, you will: build, evaluate and monitor a smart assistant; create deep research and coding agents; have a polished portfolio project

What you’ll learn

Create your own production-ready AI application in 6 weeks

🛠️ Build a Fully Functional AI Assistant from Scratch

  • A conversational AI assistant that answers questions from GitHub repositories, YouTube transcripts, or internal documentation.

  • Use Retrieval-Augmented Generation and the OpenAI API.

🤖 Add Agentic Behavior to Your AI Systems

  • Build systems that can reason, make decisions, and take actions with function calling.

  • Use tools like PydanticAI and OpenAI’s Agent SDK.

  • Extend the capabilities of your agent with MCP.

🔧 Use Testing and Evaluation to Improve Prompts and Results

  • Test the application with unit tests and judges.

  • Learn how to evaluate your application with ranking metrics, simulate user queries, and use LLMs to judge outputs.

  • Select the best prompt, model and chunking strategy using the data-driven approach.

📈 Monitor Your Application

  • Set up real-world monitoring using Grafana, Pydantic Logfire, Evidently, and LangWatch.

  • Track costs and token usage in real-time.

  • Add guardrails to prevent the application misuse.

🔧 Build 8+ Projects

  • FAQ Assistant, YouTube Video Q&A system, Wikipedia search and summary system, and a documentation agent.

  • AI Coding agent, Deep Research agent and a Code Evaluator agent.

  • Two more projects: capstone and hackathon at the end.

🎓 Ship a Capstone AI Project — Start to Finish

  • Design and build your own end-to-end AI application from scratch.

  • This could be anything from a resume reviewer to a podcast summarizer — fully tested, evaluated and monitored.

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