How to Build a Responsible AI Chatbot Using Open Technologies 7 Sections35 Lessons5 QuizzesLifetime

Curriculum

7 Sections
35 Lessons
Lifetime

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Introduction
2 Items
- 1.1
  The CABot Story
- 1.2
  How to Do This Course
Module 1 - Understanding AI
9 Items
Large Language Models (LLMs) generate language, not truth. Explore tokens, prompts, context windows and hallucinations, then examine why Retrieval-Augmented Generation (RAG) grounds responses in trusted knowledge.
Module 2 - Building the Knowledge Base
9 Items
Understand how to transform a digital resource (e.g. a book) into semantic knowledge through chunking, embeddings and vector search. Learn why CABot uses ChromaDB and metadata rather than retraining models.
Module 3 - Choosing Your Architecture
5 Items
Compare hosted APIs, local models, vector databases and orchestration frameworks. Rather than recommending one stack, this module helps you choose technologies appropriate to your own community.
Module 4 - Building the Chatbot
8 Items
In this module, we follow a learner's question from a browser prompt to asynchronous RAG orchestration. We aim to understand asynchronous architecture and browser-first deployment.
Module 5 - Responsible & Multilingual AI
5 Items
Responsible AI begins with trustworthy knowledge, not simply powerful models. Explore transparency, source attribution, multilingual retrieval, privacy, accessibility and environmentally conscious AI.
Course Conclusion & Next Steps
2 Items
- 7.1
  Lessons learned
- 7.2
  Recap: Practical workflow for building a responsible chatbot

Embeddings

An embedding is a list of numbers representing features of meaning. Imagine every passage and question placed on a giant “meaning map.” Texts about similar ideas should be closer together even when they use different words.

For example:

“How do greenhouse gases trap heat?”
“Why does carbon dioxide warm the atmosphere?”

These questions share few exact words, but their embeddings should be nearby because their meanings are related.

The Climate Chatbot uses all-MiniLM-L6-v2, which turns each chunk and each question into a 384-number vector.

Chunking

How to Build a Responsible AI Chatbot Using Open Technologies 7 Sections35 Lessons5 QuizzesLifetime

Curriculum

Embeddings

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