Build A Document Based Microsoft AI Chat Application

21 Sep 2026 04:30 AM CST

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Webinar Overview

In this webinar, we will explore how to build a document-based AI chat application using the concepts and tools covered in AI-103. Attendees will learn how to connect documents to a generative AI model, retrieve relevant information, and generate accurate, context-aware responses using Retrieval-Augmented Generation (RAG). The session will cover document ingestion, text chunking, embeddings, vector search, prompt design, and response generation. It will also include a live demonstration and practical guidance for developing secure and reliable AI chat applications.

Key Points

Introduction to Document-Based AI Chat: Understand how an AI chat application uses organizational documents to answer user questions.
Document Processing and Indexing: Learn how documents are uploaded, divided into smaller chunks, converted into embeddings, and stored for efficient retrieval.
Retrieval-Augmented Generation (RAG): Explore how relevant information is retrieved from documents and provided to a generative AI model to produce grounded responses.
Application Development: Understand how to connect the AI model, document index, prompts, and chat interface to create a complete application.
Live Demonstration and Best Practices: Follow a step-by-step demonstration and learn best practices for improving response accuracy, protecting data, and handling common challenges.

Meet The Trainer

Sharayoo Gaurav Dixit

Mentor | NIIT

Duration: March 2021 – May 2026

Delivered instructor-led training sessions on Microsoft Excel, SQL, Python, Machine Learning, Tableau, and Power BI for students and working professionals.
Designed and developed training content, assignments, exercises, and learning materials for SQL and Python for Data Science programs.
Conducted hands-on workshops and practical sessions focused on real-world data analytics and machine learning applications.
Guided learners through end-to-end industry projects, including:
- Amazon Customer Feedback Analysis – Sentiment analysis and customer insights.
- Football Data Analysis – Performance analysis and data visualization.
- IMDb Movie Data Analysis – Exploratory data analysis, visualization, and predictive modeling.
Mentored students on data cleaning, data visualization, statistical analysis, and machine learning model development.
Assisted learners in applying analytical techniques using tools such as Python, SQL, Tableau, and Power BI to solve business problems.
Evaluated student performance through assignments, assessments, and project reviews while providing continuous feedback and guidance.

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Build A Document Based Microsoft AI Chat Application

Mentor | NIIT