Artificial Intelligence & Data Science
Generative AI & Agentic Systems Course
4 Months
Advanced
Certified Course
Contact Us
+918160529351
info.nodetolearn@gmail.com
Surat, Gujarat
Course Overview
Generative AI is no longer just a buzzword — it's the skill that separates entry-level developers from high-value AI engineers. This course at NodeToLearn teaches you how to actually build with AI: connecting to real language model APIs, writing smart prompts that produce reliable outputs, coordinating multiple AI agents to complete complex tasks, and generating images or videos using diffusion models — all through hands-on practice on a dedicated workstation, one-on-one with a mentor.
You'll start by understanding how Large Language Models (LLMs) like GPT actually work — tokenization, context windows, temperature settings — and then immediately apply that knowledge by building a companion web interface that streams live AI responses. From there, you'll move into prompt engineering (the craft of getting consistent, structured outputs from AI), then into agentic systems where AI tools make decisions and call functions on their own.
The course also covers Stable Diffusion and image generation pipelines — controlling seeds, samplers, inpainting, and ControlNet — so you can build synthetic media tools, not just consume them. By the final module, you'll deploy a working multi-agent coding assistant and a live AI media dashboard, and go through mock technical interview rounds to prepare for real opportunities.
This program skips traditional database work to stay focused entirely on generative AI integration and deployment. If you want to go deep into one of the fastest-growing areas in tech — and build a portfolio that shows it — this is the course.
You'll start by understanding how Large Language Models (LLMs) like GPT actually work — tokenization, context windows, temperature settings — and then immediately apply that knowledge by building a companion web interface that streams live AI responses. From there, you'll move into prompt engineering (the craft of getting consistent, structured outputs from AI), then into agentic systems where AI tools make decisions and call functions on their own.
The course also covers Stable Diffusion and image generation pipelines — controlling seeds, samplers, inpainting, and ControlNet — so you can build synthetic media tools, not just consume them. By the final module, you'll deploy a working multi-agent coding assistant and a live AI media dashboard, and go through mock technical interview rounds to prepare for real opportunities.
This program skips traditional database work to stay focused entirely on generative AI integration and deployment. If you want to go deep into one of the fastest-growing areas in tech — and build a portfolio that shows it — this is the course.
Who is this for?
This course is built for BCA, MCA, and B.Tech (IT/CS) students who want to move beyond basic programming into AI-powered product development. It's also a strong fit for frontend developers who already know HTML/CSS/JavaScript and want to add serious AI engineering skills to their profile. Digital creators and content professionals who want to build their own image or video generation tools — rather than just use existing apps — will find the diffusion model modules especially practical. Entrepreneurs or business owners looking to automate workflows using AI agents can also benefit, provided they have some basic programming familiarity.
Career Outcomes
- Completing this Generative AI course in Surat can open paths toward roles like Generative AI Engineer
- Prompt Engineer
- AI Systems Architect
- Autonomous Agent Developer
- Synthetic Media Producer
- AI Integration Consultant
- and Freelance AI Automation Contractor. These are among the highest-paying emerging roles in tech right now
- with demand growing across product companies
- creative studios
- and consulting firms.
Course Curriculum
Module 1: How LLMs Work & Setting Up Your AI Dev Environment
- Setting up your development environment: code editors, cloud API keys (OpenAI, Anthropic), and terminal basics
- Understanding how Large Language Models (LLMs) work: transformers, tokenization, context windows, embeddings, and temperature settings — explained simply
- Building a companion web interface using HTML5 and CSS to display live AI-generated text responses
- Connecting to LLM APIs asynchronously so responses stream into your web page in real time
Module 2: Prompt Engineering — Getting Reliable, Structured Output from AI
- Writing role-based system instructions and few-shot examples to guide model behaviour consistently
- Using Chain-of-Thought (CoT) and Tree-of-Thoughts prompting to reduce AI hallucinations on complex tasks
- Forcing structured outputs like JSON or formatted markdown through precise prompt design
- Dynamically inserting user inputs and real-time data into prompt templates programmatically
Module 3: Multi-Agent Systems & Autonomous Tool Calling
- Building a single autonomous AI agent loop with its own memory, goals, and action permissions
- Coordinating networks of multiple AI agents that delegate tasks, evaluate each other's outputs, and self-correct
- Implementing tool calling — giving the AI the ability to trigger real functions (search, file operations, API calls) based on natural language
- Running parallel background AI workers without freezing or slowing down the main user interface
Module 4: Image & Video Generation with Diffusion Models
- Understanding how diffusion models (like Stable Diffusion) generate images, and writing effective positive and negative prompts
- Controlling output quality using seed values, sampler steps, and CFG (guidance scale) settings
- Editing existing images using inpainting (changing specific areas) and outpainting (extending the frame)
- Using ControlNet to force specific layouts, poses, or edge structures in generated images
Module 5: Deployment, Cost Management & Final Project Showcase
- Hosting your AI companion web app on a live cloud server with proper domain setup and frontend optimization
- Tracking user interactions and conversion events using Google Tag Manager and analytics integrations
- Monitoring and controlling API token usage costs — setting rate limits and building response caching to reduce spend
- Final project: deploy a live multi-agent coding assistant and a synthetic media dashboard; complete mock technical interview rounds