Morning
Weekend batch · Sat & Sun
IST · 9:00 AM – 12:00 PM
Live online · Starts
Live online Gen AI program
Gen AI Course Build Production-Ready Generative AI Apps
- LLMs to deployed apps in one path
- RAG, tool calling & chatbots
- Live classes with industry mentors
- A hands-on project in every module
- Program fee₹59,500No-cost EMI from ₹5,000/month
- Live classesSat & Sun9:00 AM – 12:00 PM IST
- Duration4 months6 live hours a week
- EligibilityBasic PythonNo ML experience needed
- Type of programLive onlineWith certificate
Program introduction
Move from using AI tools to building real Gen AI applications
Learn how to use Generative AI to build useful applications that can understand information, find answers, make decisions, use tools and solve real-world problems.
10modules
4months, live online
6live hours a week
The LogicMojo Gen AI Course is designed for professionals who want to go beyond simply using AI tools and learn how to build real Gen AI applications.
You will learn how Large Language Models (LLMs) work, how to get better and more reliable answers from AI, how to connect AI with your own data using RAG, and how to let AI use tools, APIs and databases through function calling.
Each module includes a hands-on project, so you learn by actually building things instead of only studying theory. By the end of the course, you will have multiple projects and a final Gen AI project that you can showcase in your portfolio and discuss in interviews.
The gap this course helps you close
Many people know how to use ChatGPT and other AI tools, but building a reliable AI application is different.
This course helps you understand what happens when AI works with real data, real users, and real-world problems. You will learn how to improve AI responses, reduce mistakes, control costs, monitor applications, and deploy your projects.
What you will learn
A structured, live, project-based program covering LLMs, prompt engineering, RAG, tool calling, chatbots, fine-tuning, multimodal AI, evaluation and LLMOps.
What makes this course different
- A project in every module: Each of the 10 modules ends with a build, and the last one is deployed.
- Taught live: Classes run live with an instructor, and every session is recorded for revision.
- Production topics are full modules: Testing AI responses, safety checks, deployment, monitoring and cost tracking each get dedicated time.
Prerequisites and Eligibility
Eligibility for the Gen AI Course
This is a practical Generative AI program. You don't need a background in machine learning, but you should be comfortable with basic Python programming.
Not sure you’re ready? Try the Python readiness check below.
To get the most out of the course, you should:
- Write basic Python functions, work with lists and dictionaries, and handle JSON
- Install packages and run Python programs from the command line
- Set aside 8–10 hours per week for classes, practice, revision and projects
Freshers, developers, data professionals, QA and DevOps engineers, technical product professionals and other learners with basic Python knowledge can follow the course. Experience with APIs, Git or SQL is helpful but not required.
Python readiness check
You're ready if you can do these without looking up every step:
- Write a function that takes a list of dictionaries and returns the ones that match a condition.
- Load a JSON file, change a field and save it again.
- Create a virtual environment,
pip installa package and run a script from the terminal. - Read a Python error message and find the line that caused it.
If two or more of these feel unfamiliar, talk to an advisor before your batch starts.
Course at a Glance
| Detail | Weekend batch |
|---|---|
| Next start date | |
| Class time (IST) | Sat & Sun, 9:00 AM – 12:00 PM |
| Duration | 4 months · 6 live hours a week · plan for 8–10 hours a week in total |
| Format | Live online classes, with lifetime access to recordings |
| Prerequisites | Basic Python. No machine-learning experience needed |
| Projects | 10 hands-on projects, one per module, ending with a deployed application |
| Support | Mentor doubt-clearing sessions, mock interviews, resume and portfolio reviews |
| Fee | ₹59,500 incl. GST (promotional, first 15 candidates) · ₹70,000 regular · no-cost EMI from ₹5,000/month |
| Extra costs | Model API and cloud usage during projects may cost extra; free tiers usually cover most of it |
| Certificate | Issued after your projects and assessments are reviewed by mentors |
| Refunds | Promotional enrollments are non-refundable; others within 7 days of the batch start · Refund policy |
Admission process
- Request a callShare your details with a learning advisor.
- Check eligibilityDiscuss your background and goals.
- Reserve your seatConfirm your start date; pay in full or via no-cost EMI.
- Start learningGet onboarded and join your first live class.
Who should enroll in this Gen AI course?
Who Is This Course For?
Built for engineers, developers and AI career builders who want to ship production-grade Generative AI applications, not just demos.
-
Software, Backend & Full-Stack Developers
Add Gen AI features to your apps using LLMs, APIs, RAG, tool calling and production-ready workflows.
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Data Scientists & ML Engineers
Move beyond notebooks and prototypes to build, evaluate, deploy and maintain Gen AI systems in production.
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QA, Automation & DevOps Engineers
Learn to test, monitor, evaluate and reliably ship LLM-powered applications.
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Product Managers & Tech Leads
Understand Gen AI capabilities, architecture, trade-offs and costs to scope and lead AI projects with confidence.
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Freshers & Career Switchers
Already know Python? Start your Gen AI journey with a structured, hands-on curriculum.
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Engineers Building Production AI
Go beyond prompting: evaluation, safety, reliability, latency and cost optimization for real users.
Is this course right for you?
What the course covers
- Using LLMs, writing good prompts and building AI apps in Python
- RAG: making AI answer questions from your own documents
- Tool calling, APIs and chatbots that remember the conversation
- Fine-tuning open models and working with text, images and speech
- Testing AI answers, keeping apps safe, and deploying them with FastAPI and Docker
What it does not cover
- Building AI models from scratch or deep math behind them
- Classical machine learning and statistics for data science
- No-code tools only. You will write Python code in every module
- A guaranteed job, placement or salary
Consider another option if
- You don't know basic Python yet and can't learn it before the batch starts
- You want a research-focused machine learning or deep learning program
- You can't give about 8–10 hours a week for 4 months
For classical ML and data science, see the Data Science Course ›
Course Highlights
Live weekend classes – 3-hour instructor-led sessions every Saturday and Sunday, 9:00 AM – 12:00 PM IST.
Lifetime access – Class recordings and course materials stay available after the course ends.
A project in every module – From an AI chat assistant to a document chatbot and a deployed final application.
Industry-standard tools – LLM APIs, LangChain, Hugging Face, vector databases, FastAPI, Docker and more.
RAG on your own data – Text splitting, embeddings, vector databases and grounded answers.
Fine-tuning & multimodal AI – Customize open models and build with text, images and speech.
Tool calling & chatbots – Function calling, external APIs and databases, plus multi-turn chat with memory.
Evaluation & safety – Hallucination testing, accuracy and relevance checks, and prompt-injection detection.
Deployment & LLMOps – FastAPI backends, Docker, deployment, monitoring and token-cost tracking.
Schedules
Fees & EMI
₹59,500₹70,000
Inclusive of GST. Regular fee ₹70,000.
The ₹59,500 promotional fee (15% off) applies to the first 15 candidates. Discounted and promotional enrollments are non-refundable unless stated otherwise at the time of purchase.
No-cost EMI from ₹5,000/month · 0% interest on credit cards
What the fee includes
- 4 months of live online classes (Sat & Sun, 3 hours each day)
- Lifetime access to class recordings
- A hands-on project in every module, ending with a deployed final project
- Dedicated doubt-clearing sessions with mentors
- GenAI + Agentic AI Course certificate of project completion
Model API and cloud usage during projects may cost extra; free tiers usually cover most of it. See the refund policy.
The LogicMojo Advantage
Mentors who build AI at work
The course mentors bring 6 to 9+ years of industry experience, including at Amazon, Microsoft and WalmartLabs. See their profiles in the mentors section.
Fits a working week
Classes run on weekends (Sat & Sun, 9:00 AM – 12:00 PM IST). Every class is recorded, and mentors run separate doubt-clearing sessions.
Career support
Mock interviews, resume and portfolio reviews, and guidance on which roles to target. Placement and salary outcomes are not guaranteed.
Gen AI course curriculum
Curriculum
You'll be able to: Explain how LLMs handle tokens and context, call an LLM API from Python and build a simple chat application.
Topics:
- What is Generative AI?
- How Large Language Models work
- GPT, Gemini and open-source models
- Tokens, context and embeddings
- Using LLM APIs
- Building your first AI application
Project
AI Chat Assistant
You'll be able to: Write prompts that produce consistent, structured JSON output, and package them as reusable templates.
Topics:
- Writing clear and effective prompts
- Zero-shot and few-shot prompting
- Giving AI the right context
- Controlling AI responses
- Structured outputs with JSON
- Creating reusable prompt templates
Project
AI Content Generator
You'll be able to: Build a RAG pipeline that splits documents, stores embeddings in a vector database and answers questions from your own data.
Topics:
- What is Retrieval-Augmented Generation?
- Working with documents and data
- Text splitting and embeddings
- Vector databases
- Finding relevant information
- Building accurate, knowledge-based responses
Project
Chat with Your Documents
You'll be able to: Let an LLM call functions, external APIs and databases, and recover cleanly when a request fails.
Topics:
- Understanding function calling
- Connecting AI with external tools
- Working with APIs
- Connecting databases with AI
- Handling errors and failed requests
- Building AI-powered workflows
Project
AI Research Assistant
You'll be able to: Build a multi-turn chatbot that keeps conversation history and handles follow-up questions.
Topics:
- Building multi-turn conversations
- Managing chat history
- Maintaining conversation context
- Adding memory to applications
- Handling follow-up questions
- Creating useful chatbot experiences
Project
AI Customer Support Chatbot
You'll be able to: Build applications that accept text, images and speech, and respond in text or speech.
Topics:
- Understanding multimodal AI
- Working with text and images
- Image understanding and generation
- Speech-to-text applications
- Text-to-speech applications
- Building AI applications with multiple input types
Project
Multimodal AI Assistant
You'll be able to: Choose between prompting, RAG and fine-tuning, prepare training data and fine-tune an open-source model with parameter-efficient methods.
Topics:
- When to use prompting, RAG or fine-tuning
- Preparing AI training data
- Introduction to fine-tuning
- Parameter-efficient fine-tuning
- Working with open-source models
- Evaluating customized models
Project
Domain-Specific AI Model
You'll be able to: Test an AI application for hallucinations, accuracy and relevance, and add checks against prompt injection.
Topics:
- Understanding AI hallucinations
- Testing AI responses
- Measuring accuracy and relevance
- Improving RAG results
- Detecting prompt injection
- Adding validation and safety checks
Project
AI Quality Evaluation System
You'll be able to: Serve an AI application through a FastAPI backend with secure key handling, streaming, rate-limit handling and cost controls.
Topics:
- Creating AI backends with FastAPI
- Connecting frontend and AI services
- Managing API keys securely
- Handling errors and rate limits
- Streaming AI responses
- Managing AI usage and costs
Project
Production-Ready AI Application
You'll be able to: Containerize and deploy an AI application, then monitor errors, performance and token costs in production.
Topics:
- Preparing AI applications for production
- Docker basics for AI applications
- Deploying AI applications
- Monitoring performance and errors
- Tracking token usage and costs
- Maintaining AI applications in production
Final Project
Build & Deploy a Complete Gen AI Application
What a complete project includes
Use this checklist for every module project. Mentors review your projects before the certificate is issued.
- Working code in a Git repository, with a README that explains how to run it
- A set of test questions or inputs, and the results your application produced
- Notes on known limitations, failure cases and approximate API costs
- A short explanation of your design choices
Who Will Mentor You in This Course?
Page written by
Ravi Singh
Data Science & AI Expert · Former AI Architect, Amazon & WalmartLabs · 15+ years in IT
I am a Data Science and AI expert with over 15 years of experience in the IT industry. I’ve worked with leading tech giants like Amazon and WalmartLabs as an AI Architect, driving innovation through machine learning, deep learning, and large-scale AI solutions. Passionate about combining technical depth with clear communication, I currently channel my expertise into writing impactful technical content that bridges the gap between cutting-edge AI and real-world applications.
Course mentors
Data scientist and senior ML engineer with industry and corporate training experience.
Ranjan Kumar
GenAI Mentor · Amazon, Microsoft · 8+ years
Senior developer with deep experience teaching applied AI and shipping production ML systems.
R&D engineer building data and ML systems at scale; specializes in LLM applications.
AI specialist focused on multi-agent systems, evaluation, and applied research.
What LogicMojo AI and data science learners say
These reviews come from learners of LogicMojo's Data Science course, not this Gen AI course, which doesn't have published reviews yet. They describe LogicMojo's teaching, not results from this course.
I highly appreciated Logicmojo's data science course for its outstanding lectures and the expert team's readiness to address technical queries, which played a crucial role in helping me secure job in Data Scientist roles especially GenAI Development.
I liked Logicmojo's data science course for its amazing lectures and the always-helpful from expert team anytime, which really helped me land a job as a Data Scientist in Invent Health Inc. It's a best data science course currently available online with best quality.
I am happy to share my experience with the Logicmojo Data Science program. it was a rewarding 7-month journey. The instructor covered Advanced Python, Machine Learning, Deep Learning, and Computer Vision in the classes. I built a strong profile as a Data Scientist and completed 5 projects during the classes. Thank you, team..
Quoted as published on the LogicMojo reviews page, where you can read more.
Gen AI Course Roadmap
Month 1
Gen AI & LLM Foundations, Prompt Engineering
Month 2
RAG, AI Tools & APIs, Chatbots
Month 3
Multimodal AI, Model Customization, Evaluation & Safety
Month 4
Production AI Apps, Deployment & Final Project
Mock Interviews
Gen AI system design & technical rounds
Resume Preparation
Portfolio & resume reviews
Job Selection
Targeting the right roles
Which Tools Will You Master in This Course?
Hands-on with the frameworks used to build, evaluate and ship Gen AI applications
- PYPython
- APIOpenAI & Gemini APIs
- LCLangChain
- HFHugging Face
- VDChromaDB & Pinecone
- PDPydantic
- WHWhisper
- RARAGAS
- FAFastAPI
- DKDocker
Tool names identify technologies taught in the course and do not imply endorsement or partnership.
Roles this course prepares you for
Career Outcomes That Matter
Skills for this role
- LLM application architecture
- Function calling and API integration
- FastAPI backends
- Cost and latency trade-offs
Relevant course projects
- AI Research Assistant
- Production-Ready AI Application
- Final deployed Gen AI application
What you will practice
- Structured JSON outputs
- Error handling and rate limits
- Docker deployment
Skills for this role
- Prompt engineering and structured outputs
- Embeddings and vector databases
- Parameter-efficient fine-tuning
- Evaluating RAG and model quality
Relevant course projects
- Chat with Your Documents
- Domain-Specific AI Model
- AI Content Generator
What you will practice
- Text splitting and retrieval
- Choosing between prompting, RAG and fine-tuning
- Preparing training data
Skills for this role
- Multi-turn conversation design
- Chat history and memory
- Text, image and speech input
- Handling follow-up questions
Relevant course projects
- AI Customer Support Chatbot
- Multimodal AI Assistant
- AI Chat Assistant
What you will practice
- Keeping context across turns
- Speech-to-text and text-to-speech
- Testing chatbot responses
Skills for this role
- Testing AI responses
- Hallucination and prompt-injection checks
- Monitoring performance and errors
- Tracking token usage and costs
Relevant course projects
- AI Quality Evaluation System
- Final deployed Gen AI application
What you will practice
- Measuring accuracy and relevance
- Docker deployment
- Maintaining apps in production
Skill mapping is based on the curriculum. It is not a guarantee of a job title, employer or salary.
What You'll Be Able to Do After This Course
- Build LLM applicationsShip assistants and APIs that return reliable, validated structured output.
- Ground answers in your dataUse embeddings and vector databases so answers come from your documents.
- Connect AI to tools and APIsUse function calling with external APIs and databases, and handle failed requests.
- Build conversational assistantsCreate multi-turn chatbots with history, memory and follow-up handling.
- Customize and go multimodalFine-tune open models and build with text, images and speech.
- Evaluate, secure and deployTest quality, add safety checks and run apps in production within budget.
Gen AI course projects
Build a portfolio that proves you can ship Gen AI
AI Research Assistant
Module 4 · An assistant that looks things up for you.
Give an LLM functions that call external APIs and a database, let it decide which to use for a question, and handle errors and failed requests so the workflow finishes cleanly.
- Function calling
- External APIs
- Error handling
AI Customer Support Chatbot
Module 5 · A chatbot that remembers the conversation.
Build a multi-turn support chatbot that keeps chat history, maintains context across follow-up questions and stores useful memory between turns.
- Chat history
- Memory
- Multi-turn context
Multimodal AI Assistant
Module 6 · Text, images and speech in one app.
Accept text, images and voice as input: describe or analyze images, transcribe speech to text and reply with generated speech.
- Vision models
- Whisper
- Text-to-speech
Domain-Specific AI Model
Module 7 · An open model adapted to one domain.
Prepare a training dataset, fine-tune an open-source model with parameter-efficient methods and compare it against prompting and RAG on the same questions.
- Hugging Face
- Parameter-efficient fine-tuning
- Model evaluation
AI Quality Evaluation System
Module 8 · Find out when your AI is wrong.
Test an AI application for hallucinations, accuracy and relevance, measure and improve RAG results, and add checks that detect prompt injection.
- RAGAS
- Hallucination testing
- Prompt-injection checks
Build & Deploy a Complete Gen AI Application
Module 10 · Your final project, live.
Bring the course together in one application with a FastAPI backend, containerize it with Docker, deploy it and monitor errors, performance and token costs.
- FastAPI
- Docker
- Monitoring
Project briefs from the course curriculum. Every module has a project; six are shown here.
Sample project walkthrough
What Kind of Project Will You Build in This Course?
1. The problem
A team keeps its policies in dozens of PDFs. People ask the same questions every day, and a plain LLM answers from general knowledge or makes things up.
2. The tools
Python, an LLM API, an embedding model, a vector database (ChromaDB or Pinecone) and, optionally, LangChain to wire the steps together.
3. What you build
- Load the PDFs and clean the extracted text.
- Split the text into chunks, create embeddings and store them.
- For each question, retrieve the most relevant chunks and instruct the LLM to answer only from them.
- Show the source passage under every answer.
- Test it with questions whose answers you already know, and fix the cases it gets wrong.
4. The result
A chat app that answers from your documents, shows where each answer came from and says it doesn't know when the documents don't cover a question.
You keep the code, the test results and a README for your portfolio.
An outline of the Module 3 project. Datasets and tool choices can vary by batch.
Certification
A credential that demonstrates capability
On successful completion, receive LogicMojo's GenAI + Agentic AI Course certificate of project completion, which you can show on LinkedIn and to recruiters. The certificate below is a sample with the learner's name replaced.
CERTIFICATE
Of Project Completion
THIS CERTIFICATE IS PRESENTED TO
Learner Name
for completing the GenAI + Agentic AI Course at Logicmojo! This certificate recognizes your dedication and hard work in mastering Generative & Agentic AI Frameworks. Your commitment to learning will undoubtedly serve you well in your future endeavors. Best of luck!
Sourav KarmakarSOURAV KARMAKARINSTRUCTOR,
LOGICMOJO ravi singhRAVI KUMAR SINGHFOUNDER,
LOGICMOJO
LOGICMOJO ravi singhRAVI KUMAR SINGHFOUNDER,
LOGICMOJO
https://www.logicmojo.com/
Signed by your instructor
Signed by a LogicMojo instructor and by founder Ravi Kumar Singh, with your course completion date.
Shareable
Add it to the Licenses & Certifications section of your LinkedIn profile.
Issued on capability
Earned only after your projects and assessments are reviewed by mentors.
Gen AI course FAQs
Frequently Asked Questions
Gen AI Training
The best Gen AI course is one that makes you build: production RAG, tool calling, evaluation and deployment — not just prompting. Look for live instruction from practicing engineers, a project in every module, and clear information on fees and schedules. LogicMojo's Gen AI course is built around those criteria, with 10 modules, a hands-on project in each and live weekend classes over 4 months.
It takes you from LLM foundations to deployed, tested Generative AI applications: prompt engineering and structured outputs, RAG, tool calling and API integration, conversational chatbots, multimodal AI, fine-tuning, evaluation and safety, and deployment & LLMOps.
Generative AI produces content — text, code, images — in response to a prompt. Agentic AI uses generative models to plan, call tools and take multi-step actions toward a goal. This course focuses on generative AI applications and covers the tool-calling foundations agents rely on in Module 4.
Traditional AI and machine-learning courses focus on predictive models: regression, classification and statistical algorithms trained on your data. Generative AI courses focus on building applications that create new content — text, code and images — with foundation models, prompt engineering, RAG, fine-tuning and tool calling. This course is a Generative AI course.
Python, OpenAI and Gemini APIs, LangChain, Hugging Face, ChromaDB and Pinecone, Pydantic, Whisper, RAGAS, FastAPI and Docker.
Yes. Module 4 covers function calling, connecting AI to external tools, APIs and databases, handling failed requests and building AI-powered workflows. Module 5 adds chat history and memory for multi-turn assistants.
Yes. Module 9 covers FastAPI backends, secure API keys, rate limits, streaming and cost controls. Module 10 covers Docker, deployment, monitoring and token-cost tracking, and the course ends with a deployed final project.
Eligibility
You should be able to write Python functions, use lists and dictionaries, read JSON, install packages and work from the command line — and reserve 8–10 hours per week for classes, revision and project work.
No prior machine-learning experience is required. Basic Python is expected; if you're unsure about your Python skills, talk to an advisor before your batch starts.
No. This is an applied course: you build with model APIs and Python frameworks such as LangChain and Hugging Face rather than deriving the math behind them. Basic Python is expected; no prior machine-learning experience is required.
Yes — motivated freshers and career switchers with working Python basics can follow the path. Prior work with APIs, Git or SQL helps but is not required.
Technical product managers and tech leads who scope AI work are a good fit. Because this is a build-oriented program, you'll need basic Python; if you're from a non-IT background, talk to an advisor before enrolling.
Format
4 months of live classes every Saturday and Sunday, 9:00 AM – 12:00 PM IST (6 live hours a week). Plan for 8–10 hours per week across live classes, revision and project work.
Yes, all classes are live online. If you miss one, you have lifetime access to class recordings and can bring questions to the doubt-clearing sessions.
Through dedicated doubt-clearing sessions with mentors, in addition to the live classes.
Fees
The fee is ₹59,500 inclusive of GST (regular ₹70,000) — 15% off for the first 15 candidates. No-cost EMI starts from ₹5,000/month, with 0% interest on credit cards.
Model API and cloud usage during projects may cost extra; free tiers usually cover most of it.
Discounted and promotional enrollments are non-refundable unless stated otherwise at the time of purchase. Other enrollments can be fully refunded if you request it in writing within 7 days of the batch start date (before the 3rd class). If a medical emergency stops you attending, you can ask to switch to a later batch, subject to approval. See the refund policy for details.
Career
Salaries vary widely by city, company, prior experience and the depth of your portfolio. Check current listings on job portals for your target roles and location. LogicMojo does not guarantee any salary or salary change.
Yes. On successful completion you receive LogicMojo's GenAI + Agentic AI Course certificate of project completion, issued after your projects and assessments are reviewed by mentors.
AI Engineer, LLM Engineer, Conversational AI Developer and LLMOps Engineer. See Career Outcomes for the skills and portfolio work mapped to each.
Yes — mock interviews, resume and portfolio reviews, and job-selection guidance. Placement and salary outcomes are not guaranteed.
No questions match your search. Ask a learning advisor
LogicMojo AI Community
Where LogicMojo learners share the AI projects they build.
Explore student profiles, GitHub repositories and AI, ML and Gen AI projects built by the LogicMojo community.
Live weekend classes · Sat & Sun, 9 AM – 12 PM IST
Ready to start building with Gen AI?
Build the skills. Ship real projects. Learn from practicing engineers. Move closer to your AI engineering goal.
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Disclaimer
LogicMojo offers practical, industry-focused training that helps learners build real skills. Career outcomes such as placements and salary growth depend on each learner's effort, prior experience and the job market at the time, so they vary from person to person. Tool and framework names on this page refer to technologies taught in the course. They belong to their owners and appear here for teaching purposes only. Their use doesn't mean those owners endorse or partner with LogicMojo.
Learner reviews and success stories linked from this page come from other LogicMojo programs and describe individual experiences; your own results will depend on your circumstances. We update the curriculum and tools regularly as the industry changes. Fees, schedules and syllabus details are governed by the written program terms provided at enrollment.