Generative AI Foundation Course for Software Developers

Instructor: Santosh Godbole CTO - Brisa Technologies

Alumnus: IIT Madras & IIM Bangalore

Course Fees: ₹9,999/-

Enroll Now

Starting On:
July 10, 2025

Format:

Online - Interactive

Language:

English

Duration:

48 Hours

Certificate:

Digital Copy

Audience:

Full-stack / Back-end / Front-end / Mobile application...

Course Overview

This hands-on, project-driven course equips learners with the practical skills and foundational knowledge to build intelligent, production-ready applications using Generative AI. Participants will explore how to work with Large Language Model (LLM) APIs, develop Retrieval-Augmented Generation (RAG) systems, and design advanced AI agents that can reason, collaborate, and interact with tools and memory. By the end of the course, learners will be able to design, build, test, and deploy AI-powered applications integrated with user interfaces — culminating in a capstone mini project that brings all components together.

What You'll Learn

Learn to integrate and work with multiple LLM APIs like OpenAI, Cohere, and Anthropic
Build production-grade Retrieval-Augmented Generation (RAG) systems using vector databases.
Understand the fundamentals of AI agents and their role in autonomous decision-making.
Enable agents to use external tools, retain memory, and perform multi-step reasoning.
Design advanced agents capable of collaboration and task delegation.
Explore methods for chaining prompts and building agent workflows.
Apply best practices in testing generative AI systems for reliability and performance.
Integrate LLM-powered backends with user interfaces for interactive applications.
Learn how to deploy generative AI solutions in real-world environments.
Complete a capstone mini project that demonstrates end-to-end system design and implementation.

Why Enroll in this Course?

Build real-world apps using powerful LLM APIs.

Master RAG systems for smarter data-driven responses.

Learn to integrate AI into frontend interfaces.

Enable backend logic with AI reasoning and tools.

Stay ahead with production-grade GenAI best practices.

Boost your career with in-demand GenAI skills.

Course Syllabus

LLM Ecosystem Overview

-Comparison of OpenAI GPT-4, Anthropic Claude, Cohere, and open-source models (Llama 3, Mistral)

API Fundamentals

Authentication patterns

Rate limiting strategies, Backup models

Cost optimization techniques

Hands-on Implementation

Python code for 3+ providers (OpenAI, Claude, Llama.cpp, Grok)

Response parsing

Error handling

LLM parameters like temperature, top-k etc

Intro to Langchain part 1

Introduction to Langchain, LLamaIndex

Building apps using Langchain modules(part 2)

Comparision with Llama index,

Introduction to RAG

How RAG improves LLM outputs

Use cases vs. limitations

Data Pipeline

Chunking strategies

Embedding models (Ada-002, BGE, allmpnet-base-v2)

Vector DBs (Chroma)

Query caching.

Retrieval Engine

Hybrid search (keyword + semantic)

Re-ranking (Cohere Rerank)

Metadata filters

Generation Layer

Context-aware prompts, output tokens

Output validation

Mitigating hallucinations

Introduction to AI Agents & Architectures

What are AI Agents? Evolution from rule-based to LLM-based agents,

Prompt engineering basics for Agents

Agent components: environment, memory, goals, tools, action loop

ReAct pattern, decision-making frameworks

Agent use cases: chatbots, copilots, task managers, automation

Live Demo: Simple rule-based and ReAct-style LLM agent using Langgraph – Vanila AI Agent

LLM Agents in Practice - Part 1

Intro to Langgraph- Building Multi Agents, tool interaction.

Live Demo: Building AI agent using Langgraph

LLM Agents in Practice - Part 2

State and Meory, UX and Human in loop,

Live Demo: Building AI agent using Langgraph

Tool Integration & External APIs

How agents use external tools (APIs, databases, search)

Creating custom tools

Error handling and retries in tool execution

Live Demo: Travel planning agent using tools like maps, weather,booking APIs

Agentic RAG

RAG VS Agentic RAG

Integration RAG pipelines into AI Agents.

Live Demo: Conversational AI agent with persistent memory along with a RAG pipeline.

Multi-Agent Systems

When and why to use multiple agents

Frameworks: CrewAI, AutoGen, LangGraph comparison

Designing workflows with role-based agents (e.g., researcher, writer, reviewer)

Live Demo: Multi-agent task-solving with CrewAI

Multi-Agent Systems

Deep dive into Autogen Studio, Building apps using Autogen

Live Demo: Building multi agentic apps using Autogen

Testing, Evaluation & Guardrails

How to test AI agents: reliability, determinism, quality

Intro to guardrails

LLM Eval – Evaluation of LLM apps

Evaluation frameworks: Guardrails, TruLens, LMQL

Prompt testing strategies and test data design

Live Demo: Building evaluation pipeline for gen ai applications

Mini Project

Hands on session where we will use all the concepts and build an app as part of mini project.

Instructor Profile

Santosh Godbole

Santosh Godbole is a veteran technologist and entrepreneur with over 30 years of experience in engineering leadership, product innovation, and strategic management. Currently serving as CTO at Brisa Technologies, he has previously co-founded OneZeroPoint and held key roles at ARRIS, Cisco, and NDS. His technical expertise spans video/image processing, IoT, networking, and agile development. With multiple US patents to his name, Santosh brings both technical depth and business insight to every learning experience.

Testimonials

Real stories from developers who upskilled, built projects, and advanced their careers with
GenAInstein Student Internship Program.

The course helped me truly understand how to work with LLMs and build GenAI apps from scratch. The hands-on projects and mentorship gave me the confidence to apply these skills at work.

Rahul M.

Software Engineer

As a student, I was overwhelmed by all the AI hype. GenAInstein's course broke it down clearly and helped me build actual working apps. I even added one to my portfolio!

Sneha K.

Final Year Student

What stood out was how practical everything was. I wasn't just learning theory—I was building things, using RAG pipelines, and even integrating APIs. This course is gold for developers.

Arjun S.

Developer

The curriculum was very well-structured, and the mentors were incredibly helpful. It's rare to find a course that balances cutting-edge content with hands-on coding so well.

Priya D.

Tech Lead

Certification Details

Certification Criteria: Minimum 75% score required Certificate

Type: Digital e-certificate (no hard copies)

Exam Date: To be announced after course completion