Generative AI Course in Coimbatore
Master LLMs, ChatGPT & AI Agents with real-time projects
About the Generative AI with Prompt Engineering Course
Master LLMs, ChatGPT, and Prompt Engineering to build the next generation of intelligent AI applications.
Train at our Avinashi Road (Hope College, Peelamedu) or Sundarapuram branch in Coimbatore — or join online — with hands-on projects, industry mentors, and dedicated placement support from day one.
Generative AI has moved from novelty to core business infrastructure in the space of a few years: companies now ship chatbots grounded in their own documents, automate content and support workflows with large language models, and expect engineers who can build with this stack. This course teaches exactly that — how LLMs like the GPT family and open-source models work, how to control them through prompt engineering, and how to build production-style applications with the OpenAI API, LangChain, Hugging Face, and vector databases such as Pinecone and ChromaDB.
The centrepiece of the curriculum is Retrieval-Augmented Generation (RAG), because it is the pattern behind most real GenAI products in 2026: connecting an LLM to private data so it answers accurately about your company, your documents, your customers. You will chunk and embed documents, store them in vector databases, wire retrieval into LLM pipelines with LangChain and LlamaIndex, and evaluate whether the system actually answers correctly — including how to reduce hallucinations. Image generation with tools like Midjourney and open-source diffusion models is covered for multimodal breadth.
This is a build-first course. Prompt engineering is taught as a rigorous discipline — structured prompts, few-shot examples, output formatting, evaluation — but you go far beyond prompting into Python-based application development, API integration, fine-tuning open models, and deploying working GenAI apps. That combination is what separates a Prompt Engineer or GenAI Developer from someone who just uses ChatGPT well.
Coimbatore’s technology ecosystem gives this skill immediate local relevance: SaaS startups and services teams around TIDEL Park Coimbatore and KGISL are adding GenAI features to client products, and established employers in the Cognizant and TCS orbit run GenAI delivery teams. Career Ladder — 10,000+ students trained, 4.6 stars across 1,500+ Google reviews — offers the course in classroom mode at Hope College (Avinashi Road) and Sundarapuram, and live online, with placement assistance covering resume building, mock interviews, and hiring-partner referrals.
Course Syllabus
Module-by-module curriculum — expand each to see the topics covered.
Module 1:Python Essentials for GenAI Development+
- Python refresher: functions, classes, and virtual environments
- Working with APIs: requests, JSON, and authentication
- Async basics for responsive AI applications
- Managing API keys and environment configuration securely
- Notebooks vs application code: structuring GenAI projects
Module 2:How Large Language Models Work+
- From word embeddings to transformers: the intuition, not just the buzzwords
- Tokens, context windows, and why they shape cost and design
- Pre-training, instruction tuning, and RLHF explained simply
- Comparing model families: GPT-class, Claude-class, Llama and open models
- Model limitations: hallucination, bias, and knowledge cutoffs
Module 3:Prompt Engineering as a Discipline+
- Zero-shot, few-shot, and chain-of-thought prompting
- Role, format, and constraint-based prompt design
- Structured outputs: getting reliable JSON from LLMs
- Prompt evaluation and iteration workflows
- System prompts and building consistent AI personas
- Prompt patterns for coding, content, analysis, and support use cases
Module 4:Building with the OpenAI API and ChatGPT+
- Chat completions, roles, and conversation state
- Function/tool calling for structured actions
- Streaming responses and handling errors and rate limits
- Cost estimation and token optimisation
- Building a customer-support chatbot end to end
Module 5:Open-Source Models with Hugging Face+
- The Hugging Face Hub: models, datasets, and spaces
- Running open models with the transformers library
- Fine-tuning basics: LoRA and parameter-efficient methods
- When open-source beats API models: cost, privacy, and control
- Serving an open model locally or on modest cloud hardware
Module 6:Embeddings, Vector Databases, and RAG+
- Embeddings: turning text into searchable vectors
- Chunking strategies for documents
- Pinecone and ChromaDB: indexing and similarity search
- Building RAG pipelines with LangChain and LlamaIndex
- Reducing hallucination with grounding and citations
- Evaluating RAG quality: retrieval accuracy and answer faithfulness
Module 7:LangChain Applications and Intro to Agents+
- Chains, prompts, and memory in LangChain
- Connecting LLMs to tools and external data
- Multi-step workflows: summarise, extract, generate
- Introduction to AutoGPT-style autonomous agents
- Where agentic systems go further — and what our Agentic AI course covers
Module 8:Image Generation and Multimodal AI+
- Diffusion models: how AI image generation works
- Midjourney and open-source image tools in practice
- Prompting for images: style, composition, and iteration
- Vision-capable LLMs: describing and analysing images
- Commercial and ethical considerations for generated media
Module 9:Capstone: Deploying a GenAI Application+
- Designing your capstone: chatbot, RAG assistant, or content engine
- Building the full pipeline with a simple web front end
- Guardrails, content filtering, and responsible AI practices
- Deployment basics and demoing your app
- Portfolio packaging and mock interview on your project
Who Should Join This Course
Real-World Projects You Will Build
- 1Document Q&A assistant: a full RAG system where users upload PDFs — policies, manuals, or college notes — and get grounded, cited answers via Pinecone/ChromaDB retrieval.
- 2Customer-support chatbot for a local business scenario: trained on a company’s FAQ and product data, with escalation logic and conversation memory.
- 3AI content engine: a tool that generates SEO-aware blog drafts, social posts, and product descriptions with structured prompts and brand-tone controls.
- 4Open-source model fine-tune: adapt a small Llama-class model to a domain task with LoRA and compare it honestly against an API model on cost and quality.
- 5Multimodal product studio: combine image generation with LLM-written copy to produce complete marketing creatives from a single brief.
- 6Capstone GenAI application of your choice, built end to end with a web interface, guardrails, and a live demo you present in a mock client review.
Career Scope & Opportunities
GenAI hiring in India has broadened from a handful of labs to nearly every services firm, product company, and startup: teams need engineers who can integrate LLMs, build RAG systems, and evaluate AI output. Common designations include GenAI Engineer, Prompt Engineer, LLM Application Developer, AI Engineer, and AI Product Analyst, alongside conventional developer roles that now list LangChain or OpenAI API experience as requirements.
Because trained supply is still thin, GenAI-skilled freshers in India typically see offers around 4-7 LPA, often above generic developer roles, while engineers with 2-4 years of experience plus demonstrable LLM project work commonly reach 10-20 LPA in the current market. Treat these as hedged, indicative ranges — the field moves quickly and portfolios drive outcomes more than in almost any other specialisation.
In Coimbatore, the opportunity is concentrated where software is built: SaaS startups and IT services teams around TIDEL Park Coimbatore and KGISL adding AI features for global clients, plus GenAI practices inside large employers like Cognizant and TCS that recruit from the region. Because most GenAI work is delivered remotely anyway, Coimbatore-based candidates compete credibly for Bengaluru, Chennai, and international remote roles without relocating.
Career Ladder’s placement assistance is tuned to how GenAI hiring works: interviews revolve around what you have built, so the focus is a portfolio of deployed projects, mock interviews covering LLM fundamentals and system design questions, and referrals to hiring partners looking specifically for these skills.
What You Will Master
100% Placement Assistance
Resume building, mock interviews, and direct referrals to our hiring partners — until you land the role.
Frequently Asked Questions
Do I need coding experience?+
Basic Python knowledge is helpful, but we start from fundamentals. Our curriculum is designed to take you from basics to advanced AI agent building.
What is the duration?+
The program is an intensive 3-month course with flexible weekday and weekend batches available.
Is placement guaranteed?+
We provide 100% Placement Support until you get placed. We have a network of 200+ hiring partners.
How is this course different from the core AI course?+
The core AI course teaches machine learning and deep learning foundations — training your own models with scikit-learn and TensorFlow. This Generative AI course specialises in working with large pre-trained models: prompting, the OpenAI API, LangChain, RAG, and fine-tuning. Choose this one if you want to build LLM applications; choose core AI if you want the underlying ML depth first.
Is prompt engineering alone enough to get a job?+
On its own, rarely. Employers want prompt engineering combined with the ability to build: API integration, RAG pipelines, and evaluation. That is why this course pairs prompting discipline with Python application development, which is the combination GenAI job descriptions actually ask for.
Do I need a powerful laptop or GPU for this course?+
No. Most work uses cloud-hosted models through APIs, and open-source experiments run on free cloud notebooks or small hosted instances. Any reasonably current laptop with a stable internet connection is sufficient.
What jobs can I apply for after completing this course?+
Typical roles include GenAI Engineer, LLM Application Developer, Prompt Engineer, and AI Engineer, plus developer and analyst roles that list GenAI skills. Your capstone and RAG projects become the centrepiece of those applications.
Will I get a certificate, and does the course stay current as AI changes?+
Yes, you receive a Career Ladder completion certificate after the modules and capstone. The syllabus is reviewed continuously because the GenAI ecosystem changes fast — the concepts you learn (prompting, RAG, embeddings, fine-tuning) are the stable foundations that transfer across model generations.
Our Coimbatore Branches
Hope College (Peelamedu)
1st Floor, 267, Avinashi Road, Opposite GRG Ladies Hostel, Above Sneha Hospital, Hope College, Coimbatore 641004
+91 88702 75880
Sundarapuram
1st Floor, Bank of Baroda Building, 5, Madukkarai Main Rd, opp. Abirami Hospital, Sundarapuram, Coimbatore
+91 88070 28071
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