Generative AI Foundation Course for Software Developers

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Generative AI Foundation Course for Software Developers(Morning)

(Online, Instructor Led Training)


Instructor: Santosh Godbole
CTO-Brisa Technologies

Alumnus: IIT Madras & IIM Bangalore




Live Interactive classes

Course Fees: ₹9999/-

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.

  • Software Developers & Engineers - Those seeking to understand and work with LLMs for integrating AI into applications.
  • Data Scientists & Machine Learning Engineers - Professionals looking to explore LLMs as part of their AI workflows and model-building.
  • AI Enthusiasts & Researchers - Individuals interested in the theoretical underpinnings and practical applications of LLMs.
  • Tech Startups & Entrepreneurs - Those considering developing AI-driven products or services utilizing LLM technologies.
  • Academics & Trainers - Individuals interested in building expertise in LLMs for teaching or conducting research.

  • 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.

Course Syllabus

LLM Ecosystem Overview
1.5 hours
-Comparison of OpenAI GPT-4, Anthropic Claude, Cohere, and open-source models (Llama 3, Mistral)
API Fundamentals
1.5 hours
Authentication patterns
Rate limiting strategies, Backup models
Cost optimization techniques
Hands-on Implementation
3 hours
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
3 hours
Building apps using Langchain modules(part 2)
Comparision with Llama index,
Introduction to RAG
1.5 minutes
How RAG improves LLM outputs
Use cases vs. limitations
Data Pipeline
1.5 hours
Chunking strategies
Embedding models (Ada-002, BGE, allmpnet-base-v2)
Vector DBs (Chroma)
Query caching.
Retrieval Engine
1.5 hours
Hybrid search (keyword + semantic)
Re-ranking (Cohere Rerank)
Metadata filters
Generation Layer
1.5 hours
Context-aware prompts, output tokens
Output validation
Mitigating hallucinations
Introduction to AI Agents & Architectures
3 hours
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
3 hours
Intro to Langgraph- Building Multi Agents, tool interaction.
Live Demo: Building AI agent using Langgraph
LLM Agents in Practice - Part 2
3 hours
State and Meory, UX and Human in loop,
Live Demo: Building AI agent using Langgraph
Tool Integration & External APIs
3 hours
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
3 hours
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
3 hours
When and why to use multiple agents
Frameworks: CrewAI, AutoGen, LangGraph comparison
3 hours
Designing workflows with role-based agents (e.g., researcher, writer, reviewer)
Live Demo: Multi-agent task-solving with CrewAI
Multi-Agent Systems
3 hours
Deep dive into Autogen Studio, Building apps using Autogen
Live Demo: Building multi agentic apps using Autogen
Testing, Evaluation & Guardrails
3 hours
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
6 hours
Hands on session where we will use all the concepts and build an app as part of mini project.
Santosh Godbole

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.

Live Interactive classes

Course Fees: ₹9999/-

Course Highlights


Course Status: Ongoing

Format: Online - Interactive

Language: English

Duration: 48 Hours

Category: GenAI

Audience: Full-stack / Back-end / Front-end / Mobile application developers

Exam Date: Immediate (after registration)

Certification Criteria: Minimum 75% score required Certificate

Type: Digital e-certificate (no hard copies)

Exam Date: To be announced after course completion

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.

Join the GenAI revolution. Start learning Large Language Models today!

 

Brought to you by GenAInstein– Igniting AI Learning and Innovation.