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

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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

Popular Vector Databases

Vector databases store embeddings and enable semantic search by finding text with similar meanings instead of exact keyword matches.

Some commonly used vector databases are (2026):

Database	Typical Use
ChromaDB	Local development, prototypes, educational projects
FAISS	Fast local similarity search library
Pinecone	Managed cloud vector database
Weaviate	Semantic search and knowledge applications
Qdrant	Production-ready vector search with metadata filtering

In this project, ChromaDB is used to store document embeddings and retrieve relevant information for the chatbot.

Popular Large Language Models (LLMs)