Python & Data Foundations
Programming, data handling and core AI concepts.
Generative AI Course · Hyderabad
Build smarter products with Generative AI.
Learn the foundations behind modern language models, prompt engineering, RAG, AI agents and practical application development — from Python concepts to deployable project workflows.
12 course modulesHands-on practiceHyderabad + online enquiries
12CURRICULUM MODULES
6PROJECT CONCEPTS
7LEARNING STAGES
AI + CODEPRACTICAL SKILL FOCUS
Understand the technology
Go beyond prompts. Learn how AI applications work.
Generative AI systems can create text, code, images and other content. Learning to use them well means understanding models, data retrieval, output reliability, tools and deployment — not simply typing into a chatbot.
This course outline follows the topics in your existing AI Fusion-X program, moving from core Python and LLM foundations into hands-on application and agent development.
Understand models and useful limitationsConnect applications to your own data with RAGBuild, test and present project work
ILLUSTRATIVE APPLICATION FLOW
01 · User QuestionINPUT
02 · Retrieve Relevant ContextRAG
03 · Generate & Validate AnswerLLM
04 · Deliver the ResponseAPP
Learning roadmap
From foundations to functioning AI applications.
Seven connected stages based on the material in your existing course page. The exact sequence can vary by batch.
Models & Embeddings
Tokens, transformers, model APIs and evaluation.
Prompt Design
Structured outputs, prompting strategies and testing.
LangChain Workflows
Chaining tools, parsers and document processing.
RAG & Vector Search
Retrieve relevant information from knowledge sources.
AI Agents
Tool calling, workflows and multi-step tasks.
Build & Deploy
APIs, interfaces, containers and practical delivery.
Complete course outline
12 modules. One connected Generative AI learning path.
Preserved from the original course syllabus. Modules, technology coverage and final project assignments should be confirmed with the trainer for the active batch.
01FOUNDATIONS
Python & AI Foundations
Python, NumPy, Pandas, OOP and essential data concepts
Build a practical Python foundation
02CORE
LLM Fundamentals
Transformers, tokenization, embeddings, GPT and BERT concepts
Understand how modern language models work
03CORE
LLM APIs & Integration
Text, vision, audio, tool calling, streaming and API fundamentals
Connect models to usable applications
04CORE
Prompt Engineering
Zero-shot, few-shot, structured output, evaluation and prompt design
Design clearer, more reliable AI interactions
05BUILD
LangChain & LlamaIndex
Document loaders, chains, parsing, retrieval and application patterns
Create connected LLM workflows
06BUILD
Retrieval-Augmented Generation
Chunking, embeddings, Pinecone, ChromaDB, FAISS, vector search
Build grounded document question-answering
07ADVANCED
AI Agents & Tool Use
Function calling, ReAct, LangGraph, CrewAI, AutoGen concepts
Prototype tool-using and multi-step agents
08ADVANCED
Fine-Tuning Concepts
LoRA, QLoRA, PEFT, evaluation and domain adaptation
Explore model customization trade-offs
09ADVANCED
Multimodal Generative AI
Vision, image generation, speech interfaces and multimodal workflows
Combine text with image and audio workflows
10ADVANCED
Open-Source LLMs
Llama, Mistral, Ollama, local inference and model selection
Run and evaluate selected open models
11PROJECTS
AI Application Development
FastAPI, Streamlit, Gradio, REST APIs, validation and testing
Build a functional interface around AI
12PROJECTS
Deployment & MLOps Basics
Docker, cloud foundations, CI/CD, observability, safety
Understand delivery and maintenance considerations
Note: The curriculum is an outline of the submitted page, not a guarantee that every listed tool or module is included in every batch. Please confirm prerequisites, coverage and schedule during the demo.
Projects & portfolio
Learn by building applications, not just reading slides.
Six representative projects retained from your existing Generative AI page. They are project concepts; exact exercises may differ across batches.
PROJECT 01
AI Chat Assistant
Build a conversational interface that uses an LLM API and keeps relevant session context.
LLM API · Python · UIPROJECT 02
Document Q&A With RAG
Index uploaded documents, retrieve relevant passages, and generate grounded responses.
Embeddings · Vector search · RAGPROJECT 03
AI Research Agent
Prototype a tool-using research workflow that gathers sources and summarizes findings.
Tool use · Planning · EvaluationPROJECT 04
Content Creation Studio
Create drafts for blogs, email and marketing content with reusable structured prompts.
Prompting · Structured outputPROJECT 05
Product Discovery Assistant
Explore semantic similarity to surface relevant items from an example catalog.
Embeddings · RetrievalPROJECT 06
Voice-Enabled AI Interface
Connect speech recognition, a language model and synthesized speech in a demo.
Audio · LLM · PythonTools & technologies
Explore the modern GenAI stack.
The original curriculum references these tools and concepts. Tool versions and course inclusion can change.
PythonLLM APIsLangChainLlamaIndexRAGChromaDBPineconeFAISSLangGraphCrewAIAutoGenFastAPIStreamlitGradioDockerOpen-source LLMs
Learning options
A learning plan that fits your next goal.
Contact AI Fusion-X to confirm the currently available modes, recorded lessons, mentor support and timings for the selected batch.
Learn from wherever you are.
Ask about online learning options, assigned recordings, practice materials and instructor support available for your batch.
Discuss Online Learning →Explore classroom learning in Dilsukhnagar.
Speak with the admissions team about classroom availability, in-person guidance, course prerequisites and suitable schedules.
Enquire About a Batch →Common questions
Generative AI course FAQs.
Useful information before you choose your next batch.
Do I need prior AI knowledge?
Beginners can start with the Python and AI foundations modules. Some advanced project exercises may be easier if you already know basic programming.
What topics are included?
The provided course outline covers 12 modules: Python, LLMs, APIs, prompt engineering, LangChain, RAG, agents, fine-tuning, multimodal AI, open-source models, application development and deployment.
Can I study online or in Hyderabad?
AI Fusion-X has offered online and Hyderabad classroom learning options. Ask the admissions team to confirm what is available for your chosen batch.
Are recordings available?
Learning resources and recorded-class access depend on the enrolled batch and the applicable access policy. Confirm the details before enrolling.
What projects will I practice?
The page illustrates six project types including chat assistants, document Q&A, AI agents, content generation, retrieval and voice interfaces. Final assigned projects can vary by batch.
Does the course include career support?
Career-focused guidance can include portfolio feedback, resume support and interview preparation. A job or placement outcome is not guaranteed.
How long is the Generative AI program?
The earlier page describes a 3–4 month program, but actual duration and timings depend on the active batch. Contact the team for the current schedule.
How do I check fees and join?
Use the Free Demo or Join Batch buttons to contact AI Fusion-X and confirm current fees, payment options, batch dates and seat availability.
Ready to start building with Generative AI?
Discuss your background, current course structure, batch availability and fee details with the AI Fusion-X admissions team.