Best Generative AI and Agentic AI Course in Bengluru

Generative AI & Agentic AI – Job-Ready Training Program

Learn Generative AI, RAG, AI Agents, LangGraph, and AWS Deployment Through Real-World Projects

At WeGuide Technologies, our GenAI & Agentic AI Training Program helps learners develop practical skills to design, build, test, and deploy AI-powered applications.

The program combines programming foundations, Generative AI concepts, prompt engineering, Retrieval-Augmented Generation (RAG), Agentic AI, API integration, and cloud deployment through hands-on labs and real-world projects.

Whether you are a fresher starting your career or an experienced IT professional looking to expand your skills into AI, this program provides a structured learning path with practical implementation experience.

Course Details

• Course Name: Generative AI & Agentic AI Training
• Duration: 14 Weeks
• Training Mode: Instructor-Led Training with Hands-On Labs
• Suitable For: Freshers, Graduates, and Experienced IT Professionals
• Training Approach: Practical Learning, Assignments, and Projects
• Projects: 3 Guided Projects + 1 Capstone Project
• Location: Marathahalli, Bengaluru
• Institute: WeGuide Technologies

Who Can Join This Course?

• Fresh graduates and final-year students.
• Software developers working with Java, .NET, Python, or other programming languages.
• DevOps engineers and cloud professionals working with AWS or Azure.
• Data Engineers and Data Analysts.
• QA engineers and test automation professionals.
• IT professionals looking to integrate Generative AI into existing applications.
• Beginners interested in learning AI application development.

What Will You Learn?

By the end of this program, you will be able to:

• Write and debug Python programs and work with APIs and SQL.
• Understand Generative AI, Large Language Models (LLMs), and prompt engineering.
• Build applications that integrate LLMs through APIs.
• Develop Retrieval-Augmented Generation applications using documents and vector search.
• Build AI agents that interact with tools, APIs, and knowledge bases.
• Design agent workflows using LangGraph.
• Develop AI applications using FastAPI and Pydantic.
• Deploy AI applications on AWS using Amazon Bedrock.
• Test, evaluate, monitor, and troubleshoot AI applications.
• Build a GitHub portfolio and explain your projects in technical interviews.

Detailed Course Curriculum

Module 1: Python Programming Fundamentals

Duration: Week 1

Topics Covered

• Introduction to Python and its applications in AI.
• Python syntax and variables.
• Data types and operators.
• Conditional statements.
• Loops and control flow.
• Functions and reusable code.
• Lists, tuples, sets, and dictionaries.
• Modules and imports.
• Exception handling.
• File handling.

Hands-On Lab

Build a student and course management application using Python.

Learning path:

• Freshers: Practise Python fundamentals through guided coding exercises.
• Experienced professionals: Complete practical programming exercises and strengthen Python skills where needed.

Module 2: Object-Oriented Programming and Python Development Tools

Duration: Week 2

Topics Covered

• Object-Oriented Programming (OOP).
• Classes and objects.
• Python packages and reusable modules.
• Virtual environments.
• Package installation and management using pip.
• Debugging techniques.
• Type hints.
• Basic asynchronous programming.
• Error handling and code organization.
• Writing reusable Python applications.

Hands-On Lab

Develop a reusable Python application with proper error handling and modular code.

Module 3: Git, REST APIs, SQL, and Database Connectivity

Duration: Week 3

Topics Covered

• Git fundamentals.
• GitHub repositories.
• Branching and merging.
• Version control best practices.
• HTTP fundamentals.
• REST API concepts.
• JSON data handling.
• API authentication.
• SQL fundamentals.
• SQL joins and aggregations.
• Database connectivity from Python.
• Working with API responses and database records.

Hands-On Lab

Create a Python API client and a database-backed application.

Module 4: Introduction to Generative AI and Large Language Models

Duration: Week 4

Topics Covered

• Overview of Artificial Intelligence, Machine Learning, and Deep Learning.
• Introduction to Generative AI.
• Generative AI use cases in business and software development.
• Large Language Models (LLMs).
• Conceptual overview of transformer architecture.
• Tokens and context windows.
• Model inference.
• Model selection.
• Understanding AI hallucinations.
• Responsible AI principles.
• Integrating hosted LLMs through APIs.

Hands-On Lab

Build a chatbot using a hosted LLM API.

Module 5: Prompt Engineering

Duration: Week 5

Topics Covered

• Introduction to prompt engineering.
• System prompts and user prompts.
• Zero-shot prompting.
• Few-shot prompting.
• Prompt templates.
• Structured JSON outputs.
• Schema validation.
• Output constraints.
• Prompt injection risks.
• Prompt testing and evaluation.
• Handling edge cases and failure scenarios.

Hands-On Lab

Build an AI-powered resume analyzer and job-description matching application.

Module 6: Retrieval-Augmented Generation (RAG) Fundamentals

Duration: Week 6

Topics Covered

• Introduction to Retrieval-Augmented Generation.
• Why RAG is used in AI applications.
• RAG architecture and workflow.
• Document parsing and extraction.
• PDF and DOCX ingestion.
• Text cleaning and preprocessing.
• Document chunking strategies.
• Metadata management.
• Embedding models.
• Preparing documents for retrieval.

Hands-On Lab

Build a document ingestion pipeline for an AI-powered knowledge application.

Module 7: Vector Databases and Semantic Search

Duration: Week 7

Topics Covered

• Introduction to vector embeddings.
• Vector search fundamentals.
• FAISS and Chroma.
• Introduction to pgvector.
• Similarity search.
• Top-K retrieval.
• Context construction.
• Connecting retrieved information with LLMs.
• Generating grounded responses.
• Basic retrieval troubleshooting.

Hands-On Lab

Build a PDF question-answering chatbot using vector search and an LLM.

Module 8: Advanced RAG and Retrieval Evaluation

Duration: Week 8

Topics Covered

• Source citations and references.
• Conversational RAG.
• Retrieval evaluation.
• Hybrid search concepts.
• Document access control.
• Reducing hallucinations.
• Handling questions when evidence is missing.
• Evaluating response relevance and accuracy.
• Improving the quality of retrieved context.
• Introduction to retrieval and reranking techniques.

Hands-On Lab

Build a company knowledge assistant that answers questions from documents and provides supporting citations.

Module Deliverable

A working RAG application that retrieves information from supplied documents, generates grounded answers, identifies supporting sources, and handles questions when sufficient evidence is unavailable.

Module 9: Introduction to Agentic AI and AI Agents

Duration: Week 9

Topics Covered

• Introduction to Agentic AI.
• AI agents vs. traditional chatbots.
• Tool calling and function calling.
• REST API integrations.
• Agent state management.
• Memory concepts.
• Planning and workflow execution.
• Connecting agents to external tools.
• Integrating knowledge bases.
• Integrating business APIs.
• Controlled execution of AI-generated actions.
• Understanding when to use workflows instead of autonomous agents.

Hands-On Lab

Build an AI agent that uses a calculator, a knowledge base, and a business API.

Module 10: LangGraph and Agent Workflow Development

Duration: Week 10

Topics Covered

• Introduction to LangGraph.
• LangGraph fundamentals.
• Nodes and edges.
• Conditional routing.
• Stateful workflows.
• Workflow execution and control.
• Retry mechanisms.
• Human approval workflows.
• Introduction to Model Context Protocol (MCP).
• Multi-agent concepts.
• Agent workflow reliability.
• Safe handling of tool calls.
• Designing controlled agent execution.

Hands-On Lab

Build an IT support agent that searches technical documentation and drafts ticket resolutions, with human approval where required.

Module 11: Amazon Bedrock and LLM Integration

Duration: Week 11

Topics Covered

• Introduction to Amazon Bedrock.
• Foundation models and model invocation.
• Accessing foundation models.
• Integrating models using Boto3.
• Embeddings with Amazon Bedrock.
• IAM permissions and access management.
• Environment configuration.
• Managing application secrets.
• API cost controls.
• Integrating Amazon Bedrock with RAG applications.
• Cloud-based LLM application architecture.

Hands-On Lab

Integrate a RAG application with an Amazon Bedrock foundation model.

Module 12: AI Application Development and AWS Deployment

Duration: Week 12

Topics Covered

• Building APIs using FastAPI.
• Data validation using Pydantic.
• Containerization using Docker.
• Amazon Elastic Container Registry (ECR).
• Deployment using Amazon EC2 or ECS Fargate.
• Amazon S3 integration.
• CloudWatch monitoring.
• Application logging.
• Basic CI/CD concepts.
• API authentication.
• Application health checks.
• Basic cloud networking.
• Error handling.
• Resource cleanup and deployment verification.

Hands-On Lab

Deploy an AI application on AWS and test its live endpoint.

Module 13: AI Application Testing, Evaluation, and Monitoring

Duration: Week 13

Topics Covered

• AI application testing fundamentals.
• Unit testing using pytest.
• Evaluation datasets.
• RAG evaluation.
• Agent workflow testing.
• Response quality evaluation.
• Latency tracking.
• Token usage and cost tracking.
• Application security considerations.
• Monitoring and debugging.
• Troubleshooting AI application failures.
• Improving application reliability.

Hands-On Lab

Test and improve an existing RAG or AI agent application.

Module 14: Capstone Project and Technical Interview Preparation

Duration: Week 14

Topics Covered

• Capstone project completion.
• End-to-end AI application architecture.
• Code review and quality improvements.
• GitHub repository documentation.
• Deployment verification.
• Architecture explanation.
• Project demonstration.
• Resume preparation.
• Technical mock interviews.
• Explaining implementation decisions and troubleshooting.

Hands-On Lab

Complete and demonstrate a deployed end-to-end AI application.

Real-World Projects

Project 1: AI Resume Analyzer

Build an AI application that analyses candidate resumes and compares them with job descriptions.

Key Features

• Resume input and text processing.
• Job-description analysis.
• LLM-based resume evaluation.
• Prompt engineering.
• Structured JSON output.
• Candidate-to-job matching results.

Technologies: Python, LLM APIs, Prompt Engineering, JSON.

Project 2: RAG Knowledge Assistant

Build an AI-powered application that answers questions using uploaded documents.

Key Features

• PDF document ingestion.
• Text extraction and chunking.
• Embedding generation.
• Vector search.
• Context retrieval.
• Grounded answers.
• Source citations.

Technologies: Python, Embeddings, FAISS or Chroma, RAG, LLM APIs.

Project 3: Agentic IT Support Assistant

Build an AI agent that searches technical documentation and prepares support ticket resolutions.

Key Features

• Knowledge-base search.
• Tool and function calling.
• API integrations.
• Agent workflow execution.
• LangGraph-based orchestration.
• Human approval before controlled actions.

Technologies: Python, LangGraph, Tool Calling, REST APIs.

Capstone Project: Deployed AI Business Application

Design, develop, test, and deploy an AI-powered business application.

Key Features

• AI-powered application workflow.
• RAG or AI agent integration.
• API development.
• Containerization.
• Cloud deployment.
• Logging and monitoring.
• Testing and documentation.
• End-to-end demonstration.

Technologies: FastAPI, Pydantic, Amazon Bedrock, RAG or AI Agents, Docker, AWS, CloudWatch.

Technology Stack

Programming and Development

• Python
• Git and GitHub
• SQL
• REST APIs
• JSON

Generative AI and LLMs

• Large Language Models
• Hosted LLM APIs
• Amazon Bedrock
• Prompt Engineering
• Structured Outputs

RAG and Vector Search

• Document Processing
• Embeddings
• FAISS
• Chroma
• pgvector
• Retrieval and Reranking

AI Frameworks and Agents

• LangChain
• LangGraph
• Tool and Function Calling
• Model Context Protocol (MCP)
• Agent Workflow Orchestration

API and Application Development

• FastAPI
• Pydantic
• PostgreSQL
• Amazon S3
• Vector Stores

Cloud and Deployment

• AWS IAM
• Amazon Bedrock
• Amazon EC2
• Amazon ECS Fargate
• Amazon ECR
• Amazon S3
• Amazon CloudWatch
• Docker
• Basic CI/CD

Testing and Monitoring

• pytest
• Retrieval Evaluation
• Agent Workflow Testing
• Application Logging
• Latency and Token-Cost Tracking
• Debugging and Monitoring

Training Methodology

The program combines instructor-led explanations, live coding, hands-on labs, assignments, project milestones, and a capstone demonstration.

• Instructor-led sessions.
• Practical implementation and coding exercises.
• Module-wise assignments.
• Guided real-world projects.
• GitHub-based project documentation.
• Testing and debugging exercises.
• Cloud deployment practice.
• Technical project reviews.

Learning Paths for Freshers and Experienced Professionals

The program uses a common classroom curriculum for learners from different backgrounds.

For Freshers and Beginners

• Guided Python and programming exercises.
• Step-by-step implementation of AI applications.
• Structured project assignments.
• Additional practice with APIs, SQL, and debugging.

For Experienced IT Professionals

• Accelerated revision of familiar programming concepts.
• Advanced implementation challenges.
• Integration with existing applications and APIs.
• Additional focus on architecture, security, testing, monitoring, and optimization.

All learners attend the same core sessions. Assignment complexity can be adjusted based on individual programming experience and assessment results.

Assessment Methodology

Students are evaluated through practical implementation and project work.

• Hands-On Labs: 30%
• Project Milestones: 25%
• Testing and Understanding: 20%
• Capstone Project: 25%

Career Preparation

The program includes structured technical interview and portfolio preparation.

• GitHub portfolio with documented projects.
• Resume preparation.
• Technical mock interviews.
• Python, SQL, and REST API interview topics.
• AI and LLM fundamentals.
• RAG and AI agent design questions.
• Capstone architecture walkthrough.
• Technical troubleshooting and project explanation.

Frequently Asked Questions

Do I need prior Generative AI knowledge?

No. The course introduces Generative AI concepts and gradually progresses to practical AI application development.

Can experienced IT professionals join this course?

Yes. Professionals from Java, .NET, DevOps, AWS, Azure, Data Engineering, QA, and other IT backgrounds can join. Their assignments can be tailored to their existing programming and technical skills.

Do I need Python knowledge?

Basic Python knowledge is helpful but not mandatory. The initial modules cover Python fundamentals. Learners with existing programming experience can complete foundation assessments and focus on the AI-specific concepts.

Will I work on real-world projects?

Yes. The program includes three guided projects and one capstone project covering an AI resume analyzer, a RAG knowledge assistant, an Agentic IT support assistant, and a deployed AI business application.

Will I learn to deploy AI applications?

Yes. The curriculum covers FastAPI, Docker, Amazon Bedrock, AWS deployment options, logging, monitoring, and testing.

Is this course suitable for DevOps and cloud engineers?

Yes. The course introduces AI application development, tool-calling agents, cloud deployment, security considerations, and monitoring, which can complement existing cloud and DevOps skills.

Is placement assistance included?

The program includes resume preparation, interview guidance, technical mock interviews, and project portfolio preparation. Any placement-related commitments should be confirmed with the institute.

Enroll Today

Take the next step toward building practical Generative AI and Agentic AI applications with WeGuide Technologies.

WeGuide Technologies – We Guide You Shine!

Location: Marathahalli, Bengaluru
Call/WhatsApp: 9148445512
Website: https://weguidetechnologies.com/

Contact us to learn more about the course, upcoming batches, and training options.