Master AI. Build with confidence.
From LLMs and RAG to Claude Code, MCP servers, and production deployment. A structured curriculum for engineers, architects, and AI leaders.
Who this is for, and why it exists.
Most AI training teaches the first 10 percent. How to call a model. How to chain a tool. How to demo an agent on a clean dataset. That is the easy part, and it is now widely available.
The other 90 percent is what determines whether an enterprise actually becomes AI-ready. How the AI organization is structured. Where the Center of Excellence sits and where it should not. Who owns model risk, data contracts, and the operating model when half your agents are autonomous and half need human review.
How to fine-tune for a regulated domain without burning two quarters on the wrong approach. How to choose between SageMaker, Vertex, Azure ML, Databricks, and the newer inference platforms when the answer depends on factors no course explains.
This is the part most training skips.
Not because it is unimportant. Because it is hard, and because the people who have lived it are usually too busy running enterprise AI programs to write 39 chapters on how.
This site is for the leader who has to make those decisions, and the architect or engineer they will rely on to execute them. The curriculum is built from production experience across healthcare, ad tech, and platform modernization at enterprise scale.
It is structured so that an engineer can go deep on MCP or fine-tuning, and a leader can read the org design, governance, and platform chapters without wading through code.
The three free chapters cover the foundation. The rest is the depth.
How AI systems evolved
Three generations of AI architecture
Each generation builds on the last. Understanding all three is what separates engineers who use AI from engineers who build with it.
LLMs
Predict the next token
- Transformer architecture processes tokens in parallel, not sequentially like RNNs
- Trained on trillions of tokens. GPT-4, Claude Opus, Gemini, Llama, DeepSeek all share this foundation
- Stateless: no memory between conversations, no access to tools or live data
What you can build
Chatbots, code completion, text summarization, translation, content generation
RAG
Retrieve, then generate
- Embeds your documents into vectors, retrieves relevant chunks at query time, and feeds them as context
- Hybrid search (BM25 + semantic) with reranking solves the "wrong document" problem that trips up naive RAG
- Still reactive: waits for prompts. No planning, no tool use, no multi-step reasoning
What you can build
Knowledge bases, support bots with citations, document Q&A, enterprise search
Agentic AI
Plan, reason, act, learn
- Agents call tools, read files, run code, and make decisions across multi-step workflows autonomously
- MCP connects agents to any system. A2A lets agents collaborate. 97M monthly SDK downloads and growing
- Self-improving: Claude's Dreaming feature reviews past sessions to find patterns. Outcomes defines success rubrics so agents can score and retry their own work
What you can build
Autonomous coding, research agents, CI/CD pipelines, multi-agent orchestration
Predicting words→Retrieving facts→Achieving goals
This course covers all three. Most training stops at the first.
39
Chapters
10
Programs
69
Lessons
20
Episodes
3
Tracks
Understand how AI actually works
The practitioner's guide to building AI systems that ship. 39 chapters take you from understanding LLMs to deploying agents, running MLOps pipelines, and governing AI across AWS, GCP, Azure, Databricks, and Snowflake. Built for engineers who build and leaders who decide.
Who this is for
Senior engineers building AI systems, architects designing platforms, and CDAIOs running enterprise AI programs. Not a beginner tutorial -- production depth from day one.
What makes it different
Written from inside the work, not from a course outline. Healthcare AI with FDA regulation frameworks, enterprise org design with real CoE blueprints, cloud platform deep dives with architecture trade-offs named. The 90% that other courses skip.
One-time purchase, lifetime access
Start with 3 free chapters. Starter ($29) unlocks building and integration. Pro ($79) unlocks everything -- all 39 chapters, 20 enterprise projects, and every future update.
39 Chapters across 7 tracks
Ch 1-3: Core Foundations
GPT, transformers, RAG, embeddings, vector DBs, agentic AI, reasoning, and safety.
3 chapters -- free to read
Ch 4-10: Building & Integration
MCP deep dive, agentic patterns, LangChain vs LangGraph, RAG Advanced, and enterprise workflows.
7 chapters
Ch 11-13: Practitioner Engineering
Python for AI, prompt engineering (11 patterns), and token efficiency with agent economics.
3 chapters
Ch 14-21: Deployment & Operations
Capstone, 21 projects, Docker, CI/CD, web UI, scaling, monitoring, MLOps, and ML deployment.
8 chapters
Ch 22-31: LLM Internals, Enterprise & Governance
Training lifecycle, fine-tuning, inference hardware, healthcare AI, enterprise org, case studies, security, and ethics.
10 chapters
Ch 32-39: Cloud Platforms & Infrastructure
Databricks, Snowflake, AWS, GCP, Azure, Data Catalogs (DuckLake, Iceberg), and AI Infrastructure.
8 chapters
Enterprise AI Capstone Projects
21 full-stack projects: healthcare, retail, ad tech, AIOps. 1,080+ tests, interactive dashboard.
Explore Projects →
Master the full Claude ecosystem
10 training programs covering prompting, Claude Code, API development, MCP servers, Skills authoring, Vertex AI, enterprise administration, and more. Pass the quiz, earn the certificate.
How it works
Pick a learning path based on your role. Complete structured lessons with hands-on exercises. Pass the master quiz (80%+ on 15-20 questions). Earn your certificate. Each program builds on the last.
Not just features -- mental models
When to use a system prompt versus CLAUDE.md. When to reach for MCP versus a custom command. When to let the agent run versus when to constrain it. The engineers who get disproportionate value understand how the pieces fit together.
120+ quiz questions
Every program has a master quiz that tests comprehension, not memorization. Scenario-based questions that reflect real engineering decisions. The certification is earned, not given.
10 Programs across 69 lessons
Claude Essentials
Prompting essentials, models and tokens, context windows, system prompts, tool use patterns, coding with Claude, apps and artifacts. The foundation every Claude user needs.
8 lessons
Claude Code Mastery
CLAUDE.md configuration, workflows, subagents, hooks, parallelization, custom commands, Agent View, GitHub integration, and automated debugging.
12 lessons
Claude API Development
Messages API, streaming responses, multi-turn conversations, tool use with Claude, RAG integration, and advanced API features like caching and batching.
7 lessons
MCP Development
Build MCP servers from scratch, client implementation, transport protocols (stdio, SSE, streamable HTTP), advanced topics, and the MCP builder skill.
6 lessons
Skills Mastery
Create your first skill, multi-file skills, sharing and distribution, skills vs features, and troubleshooting. Extend Claude with reusable capabilities.
6 lessons
Vertex AI Integration
Claude on Google Cloud: setup, conversations, prompt engineering, evaluation, tool use, RAG pipelines, and building MCP-powered agents on Vertex AI.
9 lessons
Enterprise Admin
SSO/SCIM provisioning, audit logs, seat management, managed settings, data classification, enterprise policy flow, and gateway architecture.
10 lessons
Security & Compliance
Data classification policies, acceptable use, permissions and usage controls, connector administration, and enterprise search governance.
5 lessons
Training Curriculum Design
Design AI training programs for your organization: curriculum structure, rollout strategy, change management, and measuring adoption success.
4 lessons
Podcast Series
20 deep-dive episodes covering how Claude works, RAG architecture, MCP protocol, enterprise deployment, AI safety, and the future of AI agents.
20 episodes
Evaluate AI systems rigorously
Build test datasets, run prompt evaluations, implement model-based and code-based grading, and create continuous eval pipelines. 8 weeks plus a capstone project.
The problem this solves
Your model scores 92% on benchmarks but hallucates on the 8% that matters. The gap between "works in testing" and "safe to deploy" is where most AI programs stall. Evaluation is the discipline that closes that gap.
Framework-agnostic
Works with Claude, GPT, Gemini, Llama, or any model you deploy. The evaluation patterns are universal -- systematic error analysis, automated pipelines, human review workflows, and production monitoring.
Capstone: your own eval system
The course ends with a complete evaluation system you can deploy to your own stack. Not a toy -- a production-grade pipeline with CI/CD gates, drift detection, and cost optimization built in.
8 Modules + Capstone
Foundations & Lifecycle
Why evals matter for business, the 4-phase eval lifecycle, LLM-specific challenges, and setting up instrumentation from day one
Systematic Error Analysis
Sampling strategies, open coding methodology, building failure taxonomies, and turning messy errors into actionable categories
Evaluators That Stick
Code-based grading with deterministic checks, LLM-as-judge with calibrated rubrics, and designing evaluators that survive model updates
Alignment & Collaboration
Inter-annotator agreement metrics, resolving disagreements, governance loops, and building eval culture across engineering and product
Architecture-Specific Evals
RAG evaluation with RAGAS (faithfulness, relevancy), agent eval (tool-call success), multi-turn coherence, and summarization quality
Production Monitoring
CI/CD quality gates that block bad deploys, drift detection for live models, A/B testing prompts, and automated regression pipelines
Human Review Workflows
Designing annotation workflows, red teaming methodologies, safety evaluation frameworks, and when machines need human judgment
Cost Optimization
Model cascading (cheap model first, expensive only when needed), batch APIs for bulk eval, token budgets, and ROI analysis for eval investment
Capstone Project
End-to-end eval pipeline + certificate
Capstone Implementation
Hands-on build: starter kit, LangFuse, Arize Phoenix, production code
What you will build
What you get
Everything in one platform
Not just text. Interactive lessons, podcast episodes, certification quizzes, code examples, architecture diagrams, and production checklists.
39
Deep-dive chapters
From GPT architecture to Kubernetes auto-scaling. Each chapter builds on the previous one.
69
Hands-on lessons
Code examples, exercises, and real-world scenarios across Claude Code, API, MCP, Skills, and more.
10
Certification programs
Pass the master quiz (80%+) and earn a downloadable certificate. Add it to LinkedIn or share with your team.
20
Podcast episodes
Listen while you commute. Two engineers break down every topic from prompting to production MCP servers.
AI Training Unpacked
The podcast for engineers building with Claude.
Two engineers break down everything from how Claude processes text to building production MCP servers, cost optimization, security compliance, and curriculum design. No fluff, just the concepts and code you actually need. Listen while you commute, exercise, or code.
What you can expect
S1 E1-5
Claude Essentials
How Claude works, prompting patterns, Claude Code workflows, the API lifecycle, and MCP fundamentals
S1 E6-10
Deep Dives
Skills authoring, enterprise security, production RAG, AI evaluations, and building MCP servers from scratch
S2 E11-15
Cloud and Future
Vertex AI, connectors, Claude Apps and Cowork, the future of AI agents, and agent-to-agent communication
S2 E16-20
Scale and Ship
Cost optimization (99.7% price drop), EU AI Act compliance, caching and batch, prototype to production, training programs
Your learning path
From zero to production AI
A structured path across all three training tracks. Start anywhere, go as deep as you need.
Understand
How LLMs, RAG, and agents actually work under the hood
GPT and transformer architecture
RAG, embeddings, and vector databases
Reasoning models and agentic AI
Build
MCP, agents, frameworks, RAG, and integration patterns
MCP architecture and deep dive
Agentic AI, LangChain, LangGraph
RAG Advanced and MCP Workflows
Master
Claude Code, API, MCP servers, Skills, and enterprise admin
10 training programs with quizzes
69 hands-on lessons with exercises
Downloadable certificates
Evaluate
Test datasets, LLM-as-judge grading, CI gates, and monitoring
Failure taxonomies and error analysis
Automated evaluation pipelines
Capstone: end-to-end eval system
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Covers the full AI ecosystem
Start free. Go deeper when you are ready.
Three free AI Foundations chapters to get started. Unlock Claude Training, AI Evals, and everything else with a one-time purchase.
Built by Nidhi Vichare, AI architect and trainer.