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Learning Path and Chapter Dependencies

University of Kansas School of Business

This page maps the dependencies between chapters. Use it to plan your reading order if you want to skip ahead to a specific topic.

Ch 1 (AI at a Glance)
  ↓
Ch 2 (LLM Foundations)
  ↓
Ch 3 (LLM Providers) ──→ Ch 4 (Local LLMs)
  ↓                         ↓
Ch 5 (Vibe Coding) ←────────┘
  ↓
Ch 6 (RAG First Principles)
  ↓
Ch 7 (Vector DBs) ──→ Ch 8 (Structured Data) ──→ Ch 9 (Multimedia)
  ↓
Ch 10-12 (LlamaIndex → LangChain → LangGraph)
  ↓
Ch 13 (Fine-Tuning)
  ↓
Ch 14-17 (Single Agent → Multi-Agent → Autonomous → Tool Agents/MCP)
  ↓
Ch 18 (Deployment)

What You Can Skip To

If you already have experience with a topic, here is what each chapter actually requires:

ChapterPrerequisitesCan skip if you know...
Ch 1: AI at a GlanceNoneHow LLMs are trained (pre-training, SFT, RLHF)
Ch 2: LLM FoundationsNoneTokenization, embeddings, attention, transformers
Ch 3: LLM ProvidersCh 2How to call OpenAI/Gemini APIs
Ch 4: Local LLMsCh 3How to run LM Studio or Ollama
Ch 5: Vibe CodingCh 3How to use Cursor, Copilot, or Claude Code
Ch 6: RAG First PrinciplesCh 2, Ch 3Chunking, embedding, cosine similarity, prompt stuffing
Ch 7: RAG with Vector DBsCh 6ChromaDB, Pinecone, FAISS
Ch 8: RAG with Structured DataCh 6Text-to-SQL, Cypher, schema-as-context
Ch 9: RAG with MultimediaCh 6CLIP, image/video embeddings
Ch 10: LlamaIndexCh 6LlamaIndex SimpleDirectoryReader, VectorStoreIndex
Ch 11: LangChainCh 6LCEL pipe syntax, LangChain chains and agents
Ch 12: LangGraphCh 11StateGraph, conditional edges, graph loops
Ch 13: Fine-TuningCh 2, Ch 3LoRA, QLoRA, SFTTrainer
Ch 14: Single AgentCh 3LLM + tools pattern, system prompts
Ch 15: Multi-AgentCh 14Agent handoff, LLM-as-a-judge
Ch 16: Autonomous AgentsCh 14Plan-Execute-Synthesize-Reflect pattern
Ch 17: Tool Agents and MCPCh 14MCP protocol, JSON-RPC, Cloud Run containers
Ch 18: DeploymentCh 17Cloud Run deployment, Dify workflows

Three Fast Tracks

“I just want to build a document Q&A app”: Ch 1 → Ch 2 → Ch 3 → Ch 6 → Ch 10 (LlamaIndex)

“I want to understand agents”: Ch 1 → Ch 2 → Ch 3 → Ch 14 → Ch 15 → Ch 16 → Ch 17

“I want to fine-tune a model”: Ch 1 → Ch 2 → Ch 3 → Ch 13