What will I learn in the RAG-Powered Q&A Bot Course?

You will learn how to build a RAG-powered Q&A bot using retrieval-augmented generation, LLMs, embeddings, vector databases, document retrieval, and document-based AI workflows.

Who should attend this RAG-powered Q&A bot course?

This course is suitable for students, researchers, AI learners, data science professionals, software developers, automation teams, knowledge management professionals, and participants interested in building LLM-based Q&A systems.

Does this course cover retrieval-augmented generation, embeddings, and vector databases?

Yes, the course covers retrieval-augmented generation, embeddings, and vector databases, explaining how documents can be indexed, searched, retrieved, and used by LLMs to generate context-aware answers.

Is this course useful for document-based AI workflows and LLM applications?

Yes, the course is useful for learners interested in document-based AI workflows, LLM applications, enterprise Q&A bots, semantic search, knowledge retrieval, and practical RAG system development.

RAG-Powered Q&A Bot Course

Attribute

Detail

Format

Recorded Lectures (Self-Paced)

Level

Advanced

Duration

1 Day

Certification

e-Certification + e-Marksheet

Tools

Python, LangChain, FAISS, OpenAI, FastAPI, Uvicorn

About the Rag Course

This hands‑on course teaches participants how to build, deploy, and optimize a Retrieval‑Augmented Generation (RAG) powered Q&A bot.

You will set up the environment, ingest and embed documents, create a vector store, implement a LangChain QA chain, expose it via FastAPI, and perform testing and performance tuning.

Program Highlights

• Comprehensive coverage of Hands from fundamentals to advanced applications

• Hands-on projects and real-world case studies in AI

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: Python, LangChain, FAISS, OpenAI

• Career-oriented training for academic and professional growth in AI

Course Curriculum

Module 1: Module 1 – Environment & Dependencies Setup

Create and activate a Python virtual environment
Install LangChain, FAISS‑CPU, OpenAI, FastAPI and other required packages
Configure a .env file with your OpenAI API key

Module 2: Module 2 – Document Ingestion & Embedding

Load PDF/text files from a local ./docs/ folder
Chunk documents into manageable 500‑token pieces
Generate OpenAI embeddings and store them in a FAISS index

Module 3: Module 3 – Vector Store & Retrieval Function

Initialize the FAISS vector store for fast similarity search
Implement a retrieve(query) function using index.similarity_search
Validate retrieval by printing sample chunks for test queries

Module 4: Module 4 – QA Chain Implementation

Create a prompt template that injects retrieved context
Connect the prompt to an LLMChain or RetrievalQA from LangChain
Test end‑to‑end answers with multiple user questions

Module 5: Module 5 – API Endpoint & Minimal Interface

Build a FastAPI app with a /qa POST endpoint
Integrate retrieve() and the LLM chain inside the endpoint
Validate the API using curl or Postman requests

Module 6: Module 6 – Testing, Debugging & Extensions

Handle no‑result scenarios with friendly fallback messages
Experiment with chunk sizes and k‑values for optimal retrieval
Measure latency and apply performance optimizations

Tools, Techniques, or Platforms Covered

Python
LangChain
FAISS
OpenAI
FastAPI
Uvicorn
dotenv
Bash

Real-World Applications

Apply Hands skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical AI competencies
Solve industry-relevant problems using Hands methodologies and tools
Contribute to open-source projects and collaborative research in AI
Prepare for competitive examinations, interviews, and professional certifications in AI

Who Should Attend & Prerequisites

Industry‑recognized e‑Certification + e‑Marksheet from NSTC
Hands‑on training with practical projects and real‑world datasets
Dedicated expert mentorship and doubt‑resolution throughout the day

Prerequisites:

Frequently Asked Questions

1. Do I need prior experience with LangChain?

Basic familiarity is helpful but not required; the course walks you through all essential concepts step‑by‑step.

2. What hardware is required?

A laptop/desktop with Python 3.9+, 8 GB RAM and internet access is sufficient.

3. Will I get source code?

All scripts, notebooks and the FastAPI template are provided via a GitHub repository after enrollment.

Enroll in Hands-On Course: Building a RAG-Powered Q&A Bot today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering AI skills that matter.

Brand	NSTC
Format	Recorded Lectures
Duration	1 Day
Level	Advanced
Domain	AI
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	Python, LangChain, FAISS, OpenAI, FastAPI, Uvicorn, dotenv, Bash

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Hall of Fame.

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