Tutorials

Practical engineering tutorials for AI-enabled software systems.

Step-by-step learning resources for .NET professionals building secure business applications, AI tools, RAG chatbots, integrations, and automation platforms.

15 illustrated lessonsLLM foundationsPython examples and exercises

The Illustrated LLM Tutorial

Explore tokenization, embeddings, transformers, attention, training, generation, evaluation, prompting, and fine-tuning. Finish with a tested document-assistant project.

Explore the 15-lesson LLM tutorial Start lesson 01
18 ordered lessonsPythonExamples and exercises

Python: From Your First Program to Practical Automation

Learn variables, decisions, loops, functions, collections, files, exceptions, classes, libraries, and automation. Finish with revision questions and a practical results-reporting project.

Explore the 18-lesson Python tutorial Start lesson 01
20 ordered lessonsPython + RAG + AgentsPractice included

GenAI Engineer: From Foundations to Production

Build your understanding through one practical support-assistant project. Learn the foundations, connect models to evidence, evaluate answers, add tools safely, and prepare a production release.

Includes 20 visual guides, worked examples, exercises, checkpoints, and a downloadable offline project lab.

Explore the 20-part series Start lesson 01
AI Pre-Sales 360 copilot open on the i-360 contact page
ASP.NET CoreRAGOllamaOpenAI

Build a Production-Oriented AI Chat Agent in .NET with RAG, Ollama, OpenAI, and Local Fallback

Build a website-grounded AI agent with hierarchical JSON knowledge, true parent-child retrieval, deterministic safeguards, provider-transparent vector search, a local answer composer, citations, lead handoff, analytics, and authenticated administration.

Level: IntermediateFocus: Production architectureRead: 18-22 min
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Embedding model and LLM workflow from semantic vectors through retrieval to answer generation
EmbeddingsLLMsSemantic SearchRAG

Embedding Models vs LLMs: Understand the Two Engines Behind RAG

Build a correct mental model of semantic vectors, transformer-based embedding pipelines, similarity search, token generation, grounding, and the complete retrieval-augmented generation workflow.

Level: Beginner to intermediateFocus: AI foundationsRead: 14-18 min
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Connected business systems illustrating an n8n workflow automation
n8nWorkflow AutomationFormsConditional Routing

Build a Useful Business Enquiry Workflow with n8n

Create, test, secure, and activate a practical workflow that collects an enquiry, standardizes its data, routes urgent requests, and notifies the correct team.

Level: BeginnerFocus: Business automationRead: 15–20 min
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Read the complete GenAI series in order

  1. 01Your GenAI engineering roadmap
  2. 02Generative AI fundamentals
  3. 03Python for GenAI
  4. 04Machine learning and deep learning
  5. 05Transformers explained through one ambiguous sentence
  6. 06Large language models
  7. 07Prompt engineering
  8. 08LLM APIs and structured output
  9. 09Embeddings
  10. 10Vector databases
  11. 11Build a RAG pipeline that can say “the policy does not cover that”
  12. 12Advanced RAG
  13. 13Evaluate LLM and RAG systems with cases you can defend
  14. 14AI agents
  15. 15Tool calling and MCP
  16. 16Memory and context
  17. 17Guardrails and security
  18. 18Fine-tuning
  19. 19Production GenAI
  20. 20Finish the project
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