Put generative AI to work in real products
Our Generative AI course in Hyderabad shows you how large language models work and how to build chatbots, RAG applications and content tools with them.
Ask SMALL i about this course →from llm import chat
# ask with your own documents
docs = retriever.search(“refund policy”)
answer = chat(“Summarise it”, context=docs)
print(answer)
# grounded, cited, ready to ship
What is generative AI?
Generative AI creates new text, images, audio and code. You learn how the models work and how to build useful applications around them.
Understand transformers, tokens and how LLMs generate answers.
Write prompts that give accurate, structured and repeatable results.
Let a model answer from your own documents using vector search.
Handle hallucinations, bias, privacy and safety.
Companies want assistants that read their documents, draft content and automate support. Developers who can connect models to data and ship the result are in demand.
The six steps of every project
A practical path from idea to a working application.
Pick a clear use case and what success looks like.
Select a model and decide how to call it.
Design and test prompts and templates.
Add your data with embeddings and retrieval.
Test accuracy, cost, speed and safety.
Release the app and monitor it.
11 modules in our Generative AI course in Hyderabad
Open any module to see what you will learn. Modules marked Hands on include practical lab work.
01Introduction to Generative AI
- AI, ML, Deep Learning, and Generative AI Overview
- Evolution of Generative Models, Use Cases and Ethics
02Python and Deep Learning FoundationsHands on
- Python Essentials, NumPy, Pandas
- Neural Networks, PyTorch Basics
03Transformers and Large Language Models
- Attention Mechanism, Transformer Architecture
- GPT, BERT, T5, Llama, Claude, and Gemini Model Families
- Tokenization, Embeddings, Context Windows
04Prompt EngineeringHands on
- Zero-Shot, Few-Shot, Chain-of-Thought Prompting
- System Prompts, Structured Outputs, Prompt Templates
- Prompt Evaluation and Optimization
05Working with LLM APIsHands on
- OpenAI, Anthropic, Google Gemini APIs
- Streaming, Function Calling, Tool Use
- Cost, Latency, and Rate Limit Management
06Retrieval-Augmented Generation (RAG)Hands on
- Embeddings and Vector Databases: FAISS, Pinecone, Chroma
- Chunking Strategies, Retrievers, Re-ranking
- Building Document Q&A Applications
07LangChain and LlamaIndexHands on
- Chains, Memory, Agents, Output Parsers
- Document Loaders, Indexes, Query Engines
08Fine-Tuning and Customization
- Fine-Tuning Concepts, LoRA, QLoRA, PEFT
- Open-Source Models with Hugging Face
- Dataset Preparation and Evaluation Metrics
09Multimodal Generative AI
- Text-to-Image: Diffusion Models, Stable Diffusion
- Speech, Audio, and Vision Language Models
10Deployment and Responsible AI
- Streamlit and FastAPI Apps, Docker, Cloud Deployment
- Guardrails, Hallucination Handling, Bias, Safety, and Privacy
- LLMOps and Monitoring Basics
11Capstone Projects
- Chatbot, RAG Application, Content Generation Tool, Portfolio
Tools you will use in our Generative AI course in Hyderabad
The tools used in real projects and listed in most job descriptions.
Why learn generative AI
Skills that stay useful as tools and trends change.
New skills here open doors in almost every team.
Chatbots, document Q&A and content tools you can demo.
APIs, frameworks and vector databases used by real teams.
Customer support, HR, finance, education and more.
Learn fine-tuning concepts such as LoRA and PEFT.
Guardrails and safety are part of the training.
Roles you can aim for
Titles vary by company, but these are common starting points and next steps.
Builds applications on top of language models.
Python · LLM APIsConnects models, data and user interfaces.
LangChain · FastAPIDesigns prompts and tests model behaviour.
Prompting · EvaluationBuilds retrieval pipelines for document-based answers.
Vector DBs · PythonMonitors cost, quality and reliability of AI features.
Monitoring · CloudHelps clients find and deliver AI use cases.
AI tools · CommunicationWhat you will be able to do
A balance of technical ability and the habits employers look for.
- LLM APIs: streaming, function calling and tool use.
- RAG: chunking, embeddings, retrievers and re-ranking.
- Frameworks: LangChain and LlamaIndex.
- Deployment: Streamlit, FastAPI and Docker.
- Prompt design: zero-shot, few-shot and chain-of-thought.
- Evaluation: measure quality, cost and latency.
- Safety: guardrails and hallucination handling.
- Product sense: pick use cases that add real value.
Build a portfolio you can show
Capstone work and interview preparation are part of the course.
A conversational assistant with memory and tools.
Ask questions over your own documents.
Generate and refine business content.
Work with images, speech or vision-language models.
Package your projects for employers.
Practise GenAI concept and design questions.
Who this Generative AI course in Hyderabad is for
You do not need to be an expert to begin.
Graduates curious about AI and LLMs.
Programmers adding AI features to products.
Analysts and scientists moving into GenAI.
Founders who want to prototype AI ideas.
Generative AI course in Hyderabad: your questions
Not here? Ask us on WhatsApp or talk to SMALL i.
Do I need coding experience?
Basic Python helps, and the course revisits Python essentials first.
Do I need an AI or maths background?
No. Concepts are explained step by step, starting with simple examples.
Which tools and models will I use?
You work with major model families through APIs, plus open-source models from Hugging Face, and frameworks such as LangChain and LlamaIndex.
Is generative AI a safe career bet?
Tools change quickly, but the core skills of prompting, retrieval, evaluation and deployment stay useful.
Will I get a job after the course?
No institute can promise a job. A strong portfolio and clear understanding give you the best chance.
Ready to start learning Generative AI?
Meet our trainer in a free live demo, or ask SMALL i your questions first.