GuideIntermediate
Workbook - AI Engineering Bootcamp
_To be written._
- 212
- lessons
- 22
- modules
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Content
The syllabus, module by module
Open any of them to see its lessons.
- Objectives of the Week: Introduction to AI Engineering
- Week 1 Objectives: Introduction to AI Engineering
- What is AI Engineering?
- How does AI Engineering differ from other technical roles?
- The Modern AI Stack
- Large Language Models (LLMs)
- APIs as the Foundation of Modern AI
- How AI Systems Are Built Today
- Live Review and Delivery — Week 1: Conceptual Consolidation
- Week 2 Objectives: Python Applied to AI
- Python as a Tool for AI Engineers
- HTTP Fundamentals for AI Systems
- JSON in AI Applications
- Environment Variables and Configuration
- Options for Accessing LLMs: Local vs Cloud
- Consuming AI APIs with Python
- Understanding AI API Responses
- Structured Outputs
- Live Review and Deliverable — Week 2: Complete AI Chat Script
- Week 3 Objectives: LangChain
- The Problem: Why We Need LangChain
- What is LangChain?
- Prompts as Objects
- Chains: Connecting Steps
- Intro to Memory
- Thinking in Applications
- Weekly Deliverable
- Week 03 - LangChain: From Raw Code to Abstractions
- Weekly Objectives
- What is Prompt Engineering?
- Prompts as Behavior Contracts
- Zero-Shot Prompting
- Few-Shot Prompting
- Chain-of-Thought (CoT) Prompting
- Evaluating Prompts
- Prompts in Real Systems
- From prompt to context engineering
- Week Deliverable
- Week 04 — Prompt Engineering
- Goals for the week
- Capsule description
- What is FastAPI?
- Your First Endpoint with FastAPI
- Pydantic: Data Contracts
- Error Handling and Validation
- Automatic Documentation with Swagger
- Week 5 Delivery
- Week 5 — FastAPI: From Scripts to Services
- Week 6 Objectives: FastAPI + LangChain Integration
- AI API Architecture
- Integrating LLMChain into Endpoints
- State Management: Memory in APIs
- Async Patterns for LLMs
- Testing AI Endpoints
- Limitations of the Current System
- Week 6 Deliverable: AI Technical Assistant API
- Week 6 — FastAPI + LangChain: From Simulation to Real AI
- Week 7 Objectives: Introduction to RAG
- The Static Knowledge Problem in LLMs
- What is Retrieval-Augmented Generation (RAG)?
- Embeddings: The Foundations You Need
- Chunking: Splitting Information
- Your First End-to-End RAG Flow
- The Limitations of Basic RAG
- Week 7 Deliverable: Document Q&A Script
- Week 7 — RAG: From Static Knowledge to Real Documents
- Week 8 Objectives: Vector Databases
- Why We Need Vector Databases
- What Is a Vector Database?
- Introduction to ChromaDB
- Indexing Documents with ChromaDB
- Practical Semantic Search
- Integrating ChromaDB with FastAPI
- Week 8 Deliverable: Q&A API with ChromaDB
- Week 8 — Vector Databases: From Prototype to Production with ChromaDB
- Week 9 Objectives: Intermediate RAG
- Common Problems of Basic RAG
- Advanced Chunking Strategies
- Embedding Improvements (Practical)
- Structured RAG Pipelines
- Context and Length Control
- Evaluating Document Relevance
- Reranking, contextual retrieval, and the leap to agentic RAG
- Week Deliverable: Improved RAG with Advanced Techniques
- Week 9 — Intermediate RAG: Quality and Cost Optimization
- Week 10 — Agents I: from a manual loop to a stateful agent
- What is Function Calling / Tool Use?
- Why Function Calling is Critical
- Function Calling with OpenAI
- Building Safe Tools for your Agent
- The Agent Loop: Orchestrating Tools
- From the manual loop to StateGraph
- Tools, ToolNode and the ReAct cycle
- `create_react_agent` and structured outputs
- Persistence, memory, streaming and human approval
- Mini-project: your research agent
- Week 10 — Agents I: build your stateful agent live (LangGraph)
- Week 11 — Agents II: from an agent to a system
- Subgraphs and orchestration
- Multi-agent with a supervisor
- Direct handoffs and swarm
- Protocols: MCP and A2A
- Long-term memory by tiers
- Agent security and intro to evaluation
- Mini-project: your multi-agent system
- Week 11 — Agents II: build your multi-agent system live
- Week 12 Objectives: Evaluation & Observability of AI Systems
- Why Evaluation is Critical in AI Engineering
- Metrics for RAG: Relevance, Groundedness, Completeness
- Golden Datasets and Test Cases
- LLM-as-a-Judge: Practical Evaluation
- Benchmarking Awareness: MT-Bench, HELM, and Others
- Testing Agents and Tools
- Observability and Trajectory Evaluation
- Week Deliverable: Evaluate and Improve Your Agent
- Week 12 — Evaluation & Testing: A Professional Framework for AI Systems
- Phase 2 Project Goals
- Define Scope and Architecture
- Setup: RAG + ChromaDB + FastAPI
- Implement the Retrieval Pipeline
- Integrate Tool Use and Function Calling
- Apply the Evaluation Framework
- First Functional Version
- Checkpoint: Mid-Project Review
- Professional FastAPI Structure
- Week 13 — Build: Production-ready Phase 2 Project
- Refining the System Based on Metrics
- Optimizing Performance (Latency, Cost)
- Improving Evaluation and Coverage
- Complete Technical Documentation
- Preparing the Project Presentation
- Final Demos and Presentations
- Peer Review and Feedback
- Retrospective and Phase 2 Closing
- Week 14 — Polish: Data-driven optimization + Presentation
- Weekly Objectives
- Why Docker for AI Systems
- Docker Fundamentals
- Create Dockerfiles for AI Apps
- Docker Images and Containers
- Docker Compose for Multi-Container
- Environment Configuration
- Deliverable: Dockerized Phase 2 App
- Week 15 — Dockerize the Café Aurora rag-api
- Week Objectives
- Introduction to LocalStack
- Lambda-style Execution
- API Gateway-style Routing
- S3-style Object Storage
- Service Orchestration
- Deploy AI App on LocalStack
- LocalStack vs. Real AWS: Differences
- Delivery: App Deployed on LocalStack
- Week 16 — LocalStack: serverless patterns locally
- Week 17 Objectives: Production Deployment
- From Local to Cloud: The Big Leap
- Migrate the Vector DB: ChromaDB → Pinecone
- Migrate the LLM: Local → OpenRouter
- Deploy the Backend: Render.com
- Basic Monitoring in Production
- Testing and Troubleshooting in the Cloud
- Week Delivery: Complete Deployed App
- Week 17 — Production Deployment: Render + Pinecone + OpenRouter
- Goals for the Week
- Monitoring on Render Cloud
- Dashboards and Basic Metrics
- Why AI Systems Are Expensive
- Understanding LLM Usage and Cost Drivers
- Reducing Unnecessary LLM Calls
- Caching Strategies (Redis-style)
- Request Optimization
- Cost-Aware Design
- Delivery: Optimized System
- Week 18 — Monitoring & Cost Optimization
- Week Objectives
- Security Threats in AI Systems
- Secret Management
- Environment Isolation
- Access Control Concepts
- Prompt Injection Protection
- Secure API Design
- Agentic Security: OWASP Agentic and Red-Teaming
- Delivery: Secured System
- Week 19 — Security & Hardening
- Week Objectives
- Why Integrations Matter
- Working with Third-Party APIs
- Authentication and Tokens
- Webhooks and Event-Driven Flows
- Integration with Slack/Notion/Discord
- Designing Useful Integrations
- MCP and A2A: Standard Integrations for Agents
- Weekly Submission
- Week 20 — Slack bot: an AI agent in a real workspace
- Week Objectives
- Architectural Thinking for AI Systems
- Modular System Design
- Scaling Strategies
- Reliability & Fault Tolerance
- Trade-offs in AI Architecture
- Case Studies of Real Systems
- Weekly Deliverable
- Week 21 — System Design & Scaling
- Final Project Objectives
- Integrate All the Components
- Production-Ready Checklist
- Development and Integration
- Testing and Refinement
- Complete Documentation
- Final Presentations
- Bootcamp Wrap-up
- Week 22 — Capstone: Production-ready AI Engineer
Where it fits
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Julia Catalina GastelluVerified student•Jun 23, 2026