AI Architect
Become an AI Architect by building real AI systems on an enterprise .NET / Azure / SQL Server stack.
1
AI Fundamentals
Core concepts and how modern AI systems fit an enterprise.
2 lessons
2
Large Language Models
What LLMs are, how they behave, and where they fit.
0 lessons
3
Prompt Engineering
Designing reliable prompts and system instructions.
0 lessons
4
Embeddings
Representing meaning as vectors for search and retrieval.
0 lessons
5
Vector Databases
Storing and querying embeddings at scale (incl. SQL Server vectors).
0 lessons
6
RAG Architecture
Retrieval-augmented generation: grounding, citations, evaluation.
0 lessons
7
Tool Calling
Letting models invoke functions/APIs safely.
0 lessons
8
MCP Architecture
The Model Context Protocol and tool/server design.
0 lessons
9
Agent Design Patterns
Single-agent loops, planning, and control.
0 lessons
10
Multi-Agent Systems
Coordinating multiple agents and workflows.
0 lessons
11
AI Security
Prompt injection, data exfiltration, and defenses.
0 lessons
12
AI Governance
Policy, risk, model registries, and human-in-the-loop.
0 lessons
13
Monitoring and Observability
Tracing, evaluation logs, and AI telemetry.
0 lessons
14
Azure AI Services
Azure OpenAI and the managed-AI landscape.
0 lessons
15
Enterprise AI Architecture
End-to-end reference architectures for .NET shops.
0 lessons
16
AI Adoption Strategy
Rolling AI out across a public-sector organization.
0 lessons
17
Capstone Project
The AI Architecture Blueprint — five practical solutions.
0 lessons