AI Engineering Buildcamp: From RAG to Agents
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 24h 11m | 6.26+2.25 GB
Instructor: Alexey Grigorev
This course takes you from core concepts to production-grade AI systems through hands-on, project-focused modules.
1. LLMs & RAG
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[*]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.
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2. Agentic Flows + MCP
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[*]Add agentic behavior with function calling, using libraries like PydanticAI, and Agents SDK. Expose tools via MCP.
[*]Outcome: A capable, tool-using agents.
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3. Testing & Evaluation
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[*]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.
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4. Monitoring & Guardrails
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[*]Use Grafana, Pydantic Logfire and OpenTelemetry for observability and safety.
[*]Outcome: Real-time monitoring.
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5. Use Cases
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[*]Create two agents: a website generator and a code reviewer. Learn about other use cases.
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6. Capstone
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[*]Build an end-to-end AI application with your data.
[*]Outcome: A portfolio-ready project.
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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
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[*]A conversational AI assistant that answers questions from GitHub repositories, YouTube transcripts, or internal documentation.
[*]Use Retrieval-Augmented Generation and the OpenAI API.
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Add Agentic Behavior to Your AI Systems
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[*]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.
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Use Testing and Evaluation to Improve Prompts and Results
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[*]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.
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Monitor Your Application
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[*]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.
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Build 8+ Projects
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[*]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.
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Ship a Capstone AI Project - Start to Finish
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[*]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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Who this course is for
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[*]Data Scientists and ML Engineers proficient at coding who want to integrate AI into their projects
[*]Software Engineers curious about LLMs who want to build AI applications
[*]AI Enthusiasts who are stuck at the tutorial phase and want to create something end-to-end
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Prerequisites
Coding
We will program a lot
Python, Git, Docker, command line
We will rely on these tools when building the assistant
OpenAI key or alternative
We will use OpenAI for building the AI agent
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