Hands-On Course: Building a RAG-Powered Q&A Bot

About This Course

If you’ve ever wanted a Q&A bot that doesn’t just “guess” answers—but actually looks things up before it responds—this is exactly that. In this hands-on course, you’ll build an intelligent Q&A bot powered by retrieval-augmented generation (RAG), so your bot can pull relevant context from documents and then generate answers grounded in what it finds.

You’ll go end-to-end: set up the project, ingest documents, create embeddings for vector retrieval, and connect everything to a language model so the bot can answer real-time queries in a useful, reliable way. Along the way, you’ll use modern tools like FastAPI, LangChain, FAISS, and OpenAI. And yes—performance matters here, so we’ll also tune for speed and response quality as you iterate.

Aim

The aim of this course is to equip you to build, deploy, and optimize a RAG-powered Q&A bot—from document ingestion and vector retrieval all the way to integrating language models behind a clean FastAPI interface. In other words: you won’t just understand RAG—you’ll ship a working system.

Course Objectives

By the end of this course, you will:

• Set up a Python environment and install the dependencies you need to build the project cleanly.
• Ingest documents and generate embeddings so your bot can retrieve information efficiently.
• Implement a vector store and a retrieval function designed for fast, relevant querying.
• Build a QA chain that uses retrieved context to produce better, more grounded answers.
• Expose the bot through a FastAPI endpoint so it can handle real-time queries.
• Practice testing, debugging, and performance optimization so the system holds up under real use.

Course Structure

Module 1: Environment Setup & Document Ingestion

• Setting Up the Development Environment:

  • Spin up a Python virtual environment so your dependencies stay isolated and predictable.
  • Install the core packages using pip (e.g., langchain, faiss-cpu, openai, python-dotenv, fastapi, uvicorn).
  • Create a .env file to store your OpenAI API key safely (so it’s not hardcoded into your code).

• Document Ingestion & Embedding:

  • Write a script to load text or PDF files from your ./docs/ directory.
  • Chunk documents into manageable segments (for example, ~500 tokens per chunk) so retrieval stays accurate.
  • Generate embeddings (e.g., with OpenAIEmbeddings) and store them in FAISS for fast similarity search.

• Hands-On Session:

  • Preprocess and ingest sample documents so you’re ready for retrieval and QA in the next module.

Module 2: Vector Store, Retrieval, and QA Chain Implementation

• Setting Up the Vector Store:

  • Initialize FAISS to store vectors and run similarity search.
  • Implement a retrieve(query) function that returns the most relevant chunks quickly.

• QA Chain Implementation:

  • Define a QA chain that uses retrieved context to answer questions more accurately.
  • Integrate LLMChain or RetrievalQA (via LangChain) so your pipeline becomes query → retrieve → answer.

• Hands-On Session:

  • Ask 2–3 different questions, inspect the retrieved context, and verify the answer quality (this is where it starts to feel real).

Module 3: API Endpoint, Testing, and Performance Optimization

• Building the API Endpoint:

  • Scaffold a FastAPI app and expose a /qa POST endpoint for questions.
  • Wire retrieval + the chain into the endpoint so your bot answers in real time.

• Testing and Debugging:

  • Test with curl or Postman to confirm request/response behavior.
  • Handle edge cases (like “no context found”) gracefully—because users will hit them.

• Performance Optimization:

  • Experiment with chunk size and k (top results returned) to balance speed vs. relevance.
  • Measure latency and tighten bottlenecks so the bot feels responsive.

• Hands-On Session:

  • Run a full performance check and make targeted tweaks for smoother query handling.

Who Should Enrol?

• Developers, data scientists, and AI enthusiasts who know the basics of Python and have a general grasp of machine learning concepts.

• Anyone who wants hands-on experience building AI-driven Q&A systems that feel genuinely useful.

• Learners familiar with APIs, NLP, and vector databases like FAISS (helpful, but not required).

• Students and researchers who want to understand AI models, generative systems, and what it takes to deploy them in real time.