Module 5 Wrap-up: Building with Intelligence
Reviewing the AI tech stack and building a simple RAG pipeline and local AI setup.
Module 5 Wrap-up: The AI Architect
You have moved from "Chatting" to "Building." You know that a modern AI application is more than just a model—it is a system of Retrieval (RAG), Storage (Vector DBs), and Action (Agents).
Hands-on Projects: The Architect's Lab
Project 1: Run your first Local Model
- Download Ollama (ollama.com).
- Open your terminal and type:
ollama run llama3. - Ask the local model: "Write a poem about privacy."
- The Goal: Feel the speed of AI running directly on your silicon.
Project 2: Design a RAG Workflow
- Identify a Data Source: (e.g., A folder of your meeting notes or a long PDF).
- The Plan:
- How would you chunk the data? (Paragraph by paragraph).
- Where would you store the embeddings? (Chroma).
- What would the system prompt look like? ("Answer based ONLY on the meeting notes provided...")
Module 5 Summary
- RAG connects LLMs to your private data safely.
- Vector Databases enable semantic search (search by meaning).
- Agents use LLMs to make autonomous tool-calling decisions.
- LangChain is the orchestrator for all AI components.
- Local LLMs (Ollama) provide the ultimate in privacy and cost-control.
💡 Guidance for Learners
Module 5 is the "Entry Level" for the Agentic AI course (Course 3). If you enjoyed building these pipelines, that is your next graduation point!
Coming Up Next...
In Module 6, we look at the big picture. We will discuss Ethics, Security, and the Future of how we work alongside these intelligent machines.
Module 5 Checklist
- I can describe the 3 steps of RAG (Retrieve, Augment, Generate).
- I understand how a Vector Database differs from Excel.
- I know what a LangChain "Agent" is.
- I have installed or researched how to run Ollama.
- I can explain the benefit of Running AI locally.