Course

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

Works two ways: as a standalone short course in practical AI/GenAI literacy, or as an add-on for graduates of Fourcup's testing, development or cloud/DevOps tracks who want to layer AI skills onto their main course. Covers what LLMs are and aren't, prompt engineering, evaluating AI output critically, using AI coding/testing/ops assistants day to day, RAG (embeddings, vector databases), calling LLM APIs with streaming and tool-calling, a beginner-level intro to AI agents (LangGraph, AutoGen, CrewAI, MCP), and responsible AI use — scoped for practical fluency, not full ML engineering depth.

⏱ 3-4 Weeks 🖥 Online 💻 3 Projects ⭐ 4.7/5

🎓 Book Free Demo 📞 +91 75502 51906

INCLUDED AT NO COST

Everything below is included at no extra cost

🚀 FREE!

Live Project(s)

💼 FREE!

Job Reference(s)

🎓 FREE!

Career Guidance

♾️ FREE!

Life Time Support

PACKAGES

We Offer Responsive Packages With An Unexplored Syllabus

Shortest track

⏱ 9 Days

★ 4.6/5

AI Literacy & Practical GenAI Tools

The essentials — how LLMs work, prompt engineering, and using AI coding/testing/ops assistants day to day.

AI & GenAI Literacy Fundamentals
Practical GenAI Tools for Your Discipline
Interview Prep: Talking About Your AI Skills

ChatGPT, Claude, Gemini, GitHub Copilot, Claude Code

View Curriculum →

Most enrolled

⏱ 10 Days

★ 4.7/5

AI-Augmented Workflow + RAG Basics

Practical GenAI tool use plus grounding AI answers in real data with embeddings and vector databases.

AI & GenAI Literacy Fundamentals
Practical GenAI Tools for Your Discipline
RAG (Retrieval-Augmented Generation) Basics
Interview Prep: Talking About Your AI Skills

ChatGPT, Claude, Gemini, Pinecone, Weaviate, Chroma

View Curriculum →

Complete add-on

⏱ 18 Days

★ 4.8/5

Build Your First AI Feature

Go from using AI tools to shipping one — API integration, streaming, tool-calling, RAG and an intro to AI agents.

AI & GenAI Literacy Fundamentals
Practical GenAI Tools for Your Discipline
RAG (Retrieval-Augmented Generation) Basics
Building a Simple AI Feature
Intro to AI Agents
AI Governance & Responsible Use
Interview Prep: Talking About Your AI Skills

OpenAI API, Claude API, Gemini API, Pinecone, Weaviate, Chroma, LangGraph, AutoGen, CrewAI, MCP

View Curriculum →

Customized

⏱ Custom

★ 4.7/5

Build Your Own (Customized)

Pick exactly the modules you need from the AI add-on module bank, priced and scheduled to match.

Choose any modules from the shared bank
Custom duration & pricing
Same trainers, same mock-interview access
Interview Prep: Talking About Your AI Skills included

Selected per module

View Curriculum →

LEARNING OUTCOMES

What you will be able to do

Concrete skills, not topic names — each one is something you can show an interviewer.

🎯

Design test cases that find real defects

Boundary values, equivalence classes and edge cases — not just happy-path coverage.

🧪

Automate with a real framework

Page Object Model, reusable helpers and a suite you can actually maintain.

📊

Read a defect report the way a developer will

Reproduction steps, severity and evidence — reports that get fixed, not ignored.

🔧

Build and maintain a test suite

Structure tests so they survive the product changing under them.

🔗

Test APIs, not just interfaces

Postman and REST Assured, including auth flows most courses skip.

📈

Run suites in a CI pipeline

Jenkins and GitHub Actions — tests that run on every commit automatically.

TOOLS & SKILLS

Tools and skills covered

Java

Selenium

TestNG

Postman

Git

Jenkins

JMeter

SQL

🏆

Certificate of Completion

Awarded once every module and project is complete — recognised by our hiring partners.

IS THIS FOR YOU?

Who this course is for

If none of these describe you, tell us — we will point you at a better fit rather than sell you this one.

🎓

Freshers from any engineering branch

🔁

Manual testers hitting the automation ceiling

💻

Support engineers moving into QA

WHY IN FOURCUP?

Software Testing Program

Practical exposure to actual work environment

Work on live, under-construction projects with the exposure of an actual work environment, alongside a freelancer.

Dynamic syllabus structure

Built with multiple industry experts, so students become genuinely fluent in the subject rather than exam-ready.

Expert teachers who are active practitioners too

Mentors bring real industry experience and share their ups, downs and delivery skills, not just slides.

GET STARTED

Get Started Today

Download our Syllabus and review it thoroughly.

Request Call Back / Apply the Enquiry Form.

Join Our Free Demo Class.

Understand the Opportunity.

Join the Training session and become an expert!

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AI, ML & Soft Skills

AI, ML & Soft Skills — Complete Career Accelerator

A complete AI/ML Engineer flagship program covering Python and statistical foundations, classical machine learning with scikit-learn, XGBoost and LightGBM, deep learning with PyTorch, the full LLM/GenAI engineering stack (Hugging Face, LangChain, LlamaIndex, fine-tuning, end-to-end RAG pipeline design), vector databases (Pinecone, Weaviate, Chroma, Milvus), MLOps (FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX), AI agent frameworks (LangGraph, AutoGen, CrewAI, MCP) and responsible AI — folded together with professional communication, behavioral interview mastery and resume/portfolio building, so technical depth and interview readiness are built in one program instead of two.

PythonNumPypandasscikit-learnXGBoost+19 more

15% OFF Flagship AI/ML program

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

A focused Soft Skills & Interview Readiness program covering professional communication for technical roles, group discussion and aptitude fundamentals, the STAR method for behavioral interviews, body language and virtual-interview presence, salary negotiation and offer evaluation, workplace professionalism, and resume/LinkedIn optimization — anchored by a mock interview tailored to your own track (developer, tester or cloud/DevOps), not a generic HR-round rehearsal. Designed to work equally well as a standalone short course or as an add-on graduates of other Fourcup tracks bolt onto their main course.

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

A complete, current front-end and UI/UX program covering modern JavaScript/TypeScript, React 19, Next.js (App Router, SSR/SSG/ISR, Server Components), Tailwind CSS and shadcn/ui, UI/UX fundamentals (usability heuristics, Figma-to-code, design systems, responsive design), accessibility (WCAG 2.2) and Core Web Vitals as first-class engineering skills, component and end-to-end testing with Playwright and Storybook, and an AI-assisted front-end workflow (Claude Code, GitHub Copilot, Cursor, v0) with the same "review AI output like an editor" discipline throughout — so you graduate able to design, build, test and ship real production interfaces.

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

15% OFF High-demand skill set

CURRICULUM

Module by module

Open any module to see exactly what it covers. Nothing is hidden behind a "download the syllabus" form.

9 Days

DURATION

MODULES

REAL-TIME PROJECTS

MOCK INTERVIEWS

Shortest track

AI Literacy & Practical GenAI Tools

⏱ 9 Days ★ 4.6

3 modules

Most enrolled

AI-Augmented Workflow + RAG Basics

⏱ 10 Days ★ 4.7

4 modules

Complete add-on

Build Your First AI Feature

⏱ 18 Days ★ 4.8

7 modules

Customized

Build Your Own (Customized)

⏱ Custom ★ 4.7

0 modules

1 AI & GenAI Literacy Fundamentals 3 days ▾

Topic	What You'll Learn
1. What LLMs Actually Are (and Aren't)	How large language models generate text, why they "hallucinate," and the difference between a model that sounds confident and one that's correct.
2. Prompt Engineering Basics	Writing clear, specific prompts, giving context and examples, and iterating when the first output misses.
3. Evaluating AI Output Critically	Spotting confident-sounding wrong answers, checking claims against a real source, and knowing when to trust an AI's output and when not to.
4. The Current GenAI Landscape	A practical map of chat assistants, coding assistants and API-based models, and where each one fits into daily work.
4.1) Why this module exists	Most professionals now use AI tools daily without ever learning how they actually work or fail. This module is the foundation every other module in this course builds on.

2 Practical GenAI Tools for Your Discipline 4 days ▾

Topic	What You'll Learn
1. AI Coding Assistants for Developers	Using GitHub Copilot, Claude Code and Cursor for real feature work, and reviewing generated code like an editor, not accepting it on trust.
2. AI-Augmented Test Generation for Testers	Using AI to draft test cases, generate test data and spot edge cases a manual pass might miss.
3. AI-Assisted Infra & Ops Tooling for Cloud/DevOps	Using AI assistants to draft scripts, explain unfamiliar logs and configs, and speed up routine infrastructure tasks.
4. Choosing the Right Tool for the Task	Matching a GenAI tool to the job at hand instead of using one tool for everything.
4.1) How this module works	Delivered generically enough to apply across development, testing and cloud/DevOps backgrounds, with examples drawn from whichever discipline the learner already works in.

3 Interview Prep: Talking About Your AI Skills 2 days ▾

Topic	What You'll Learn
1. How to Talk About AI Skills in an Interview	Framing practical GenAI experience (tool use, RAG, a shipped AI feature) the way a hiring manager actually listens for it.
2. Common AI-Related Interview Questions	What interviewers now ask about AI tool use, prompting and responsible use, across dev, test and cloud/DevOps roles.
3. Resume Additions for AI Literacy	Adding this add-on's skills to an existing resume without overstating your depth.
4. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.

1 AI & GenAI Literacy Fundamentals 3 days ▾

Topic	What You'll Learn
1. What LLMs Actually Are (and Aren't)	How large language models generate text, why they "hallucinate," and the difference between a model that sounds confident and one that's correct.
2. Prompt Engineering Basics	Writing clear, specific prompts, giving context and examples, and iterating when the first output misses.
3. Evaluating AI Output Critically	Spotting confident-sounding wrong answers, checking claims against a real source, and knowing when to trust an AI's output and when not to.
4. The Current GenAI Landscape	A practical map of chat assistants, coding assistants and API-based models, and where each one fits into daily work.
4.1) Why this module exists	Most professionals now use AI tools daily without ever learning how they actually work or fail. This module is the foundation every other module in this course builds on.

2 Practical GenAI Tools for Your Discipline 4 days ▾

Topic	What You'll Learn
1. AI Coding Assistants for Developers	Using GitHub Copilot, Claude Code and Cursor for real feature work, and reviewing generated code like an editor, not accepting it on trust.
2. AI-Augmented Test Generation for Testers	Using AI to draft test cases, generate test data and spot edge cases a manual pass might miss.
3. AI-Assisted Infra & Ops Tooling for Cloud/DevOps	Using AI assistants to draft scripts, explain unfamiliar logs and configs, and speed up routine infrastructure tasks.
4. Choosing the Right Tool for the Task	Matching a GenAI tool to the job at hand instead of using one tool for everything.
4.1) How this module works	Delivered generically enough to apply across development, testing and cloud/DevOps backgrounds, with examples drawn from whichever discipline the learner already works in.

3 RAG (Retrieval-Augmented Generation) Basics 1 wk ▾

Topic	What You'll Learn
1. What RAG Solves	Why grounding an AI's answers in real data fixes the hallucination problem that plain prompting can't.
2. Embeddings, Explained Simply	What a text embedding is and why it lets a computer compare meaning, not just keywords.
3. Vector Databases	A practical look at Pinecone, Weaviate and Chroma, and what a vector database actually stores and retrieves.
4. Building a Minimal RAG Pipeline	Loading documents, generating embeddings, retrieving relevant chunks and feeding them to an LLM for a grounded answer.
4.1) Why this module exists	RAG is the single most common way real products ground AI answers in a company's own data, and it's approachable at a beginner level once embeddings click.

4 Interview Prep: Talking About Your AI Skills 2 days ▾

Topic	What You'll Learn
1. How to Talk About AI Skills in an Interview	Framing practical GenAI experience (tool use, RAG, a shipped AI feature) the way a hiring manager actually listens for it.
2. Common AI-Related Interview Questions	What interviewers now ask about AI tool use, prompting and responsible use, across dev, test and cloud/DevOps roles.
3. Resume Additions for AI Literacy	Adding this add-on's skills to an existing resume without overstating your depth.
4. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.

1 AI & GenAI Literacy Fundamentals 3 days ▾

Topic	What You'll Learn
1. What LLMs Actually Are (and Aren't)	How large language models generate text, why they "hallucinate," and the difference between a model that sounds confident and one that's correct.
2. Prompt Engineering Basics	Writing clear, specific prompts, giving context and examples, and iterating when the first output misses.
3. Evaluating AI Output Critically	Spotting confident-sounding wrong answers, checking claims against a real source, and knowing when to trust an AI's output and when not to.
4. The Current GenAI Landscape	A practical map of chat assistants, coding assistants and API-based models, and where each one fits into daily work.
4.1) Why this module exists	Most professionals now use AI tools daily without ever learning how they actually work or fail. This module is the foundation every other module in this course builds on.

2 Practical GenAI Tools for Your Discipline 4 days ▾

Topic	What You'll Learn
1. AI Coding Assistants for Developers	Using GitHub Copilot, Claude Code and Cursor for real feature work, and reviewing generated code like an editor, not accepting it on trust.
2. AI-Augmented Test Generation for Testers	Using AI to draft test cases, generate test data and spot edge cases a manual pass might miss.
3. AI-Assisted Infra & Ops Tooling for Cloud/DevOps	Using AI assistants to draft scripts, explain unfamiliar logs and configs, and speed up routine infrastructure tasks.
4. Choosing the Right Tool for the Task	Matching a GenAI tool to the job at hand instead of using one tool for everything.
4.1) How this module works	Delivered generically enough to apply across development, testing and cloud/DevOps backgrounds, with examples drawn from whichever discipline the learner already works in.

3 RAG (Retrieval-Augmented Generation) Basics 1 wk ▾

Topic	What You'll Learn
1. What RAG Solves	Why grounding an AI's answers in real data fixes the hallucination problem that plain prompting can't.
2. Embeddings, Explained Simply	What a text embedding is and why it lets a computer compare meaning, not just keywords.
3. Vector Databases	A practical look at Pinecone, Weaviate and Chroma, and what a vector database actually stores and retrieves.
4. Building a Minimal RAG Pipeline	Loading documents, generating embeddings, retrieving relevant chunks and feeding them to an LLM for a grounded answer.
4.1) Why this module exists	RAG is the single most common way real products ground AI answers in a company's own data, and it's approachable at a beginner level once embeddings click.

4 Building a Simple AI Feature 1 wk ▾

Topic	What You'll Learn
1. Calling an LLM API	Making your first calls to the OpenAI, Claude and Gemini APIs, and handling keys, costs and rate limits properly.
2. Streaming Responses	Building a chat-style output that streams tokens as they arrive instead of waiting for the full response.
3. Basic Tool-Calling / Function-Calling	Letting a model call a function in your own code (look up a record, run a calculation) under your program's control.
4. Putting It Together	Assembling a small, working AI-powered feature end to end, from API call to a usable output.
4.1) Where this shows up in real jobs	"Add AI to this" is now a routine ask across roles. This module is the difference between talking about AI and having shipped something with it.

5 Intro to AI Agents 4 days ▾

Topic	What You'll Learn
1. What an Agentic Workflow Is	The difference between a single prompt-response call and a model that plans, calls tools, and acts across multiple steps.
2. Agent Frameworks at a Glance	Beginner-level concepts behind LangGraph, AutoGen and CrewAI, without the depth of a dedicated agent-engineering course.
3. MCP (Model Context Protocol) as an Emerging Standard	What MCP is trying to standardize and why it matters for how agents connect to tools and data.
4. Where Agents Fit (and Where They Don't Yet)	Realistic use cases for agentic workflows today, and where a simpler API call is still the better choice.
4.1) Scope note	This module is a beginner-appropriate introduction, not full agent-engineering depth — that level of depth belongs in a separate, dedicated course.

6 AI Governance & Responsible Use 3 days ▾

Topic	What You'll Learn
1. Data Privacy with AI Tools	What happens to data you paste into a public AI tool, and why company and customer data need extra care.
2. Knowing When Not to Trust AI Output	Recognizing the situations (legal, medical, financial, security-sensitive) where AI output needs a human expert, not just a double-check.
3. Company AI Policies in Practice	Why most employers now have an AI usage policy, and how to work within one responsibly.
3.1) Why this module exists	Using AI tools well includes knowing their limits and risks, not just their capabilities.

7 Interview Prep: Talking About Your AI Skills 2 days ▾

Topic	What You'll Learn
1. How to Talk About AI Skills in an Interview	Framing practical GenAI experience (tool use, RAG, a shipped AI feature) the way a hiring manager actually listens for it.
2. Common AI-Related Interview Questions	What interviewers now ask about AI tool use, prompting and responsible use, across dev, test and cloud/DevOps roles.
3. Resume Additions for AI Literacy	Adding this add-on's skills to an existing resume without overstating your depth.
4. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.

The curriculum for this package has not been added yet.

WHAT YOU GET

This Course Includes

💻

Online

🎯

3-4 Weeks Duration (Flexible)

🖥

3 Real Industry Projects

🏆

Certificate on Completion

♾️

Lifetime Access to Recordings

💼

Job References to 50+ Companies

🎯

5 Mock Interview Rounds

🗣

Soft Skills & Communication

📱

WhatsApp Batch Support

📥

Brochure & Syllabus Download

🛠 Tools Covered:

ChatGPTClaudeGeminiGitHub CopilotClaude CodePineconeWeaviateChromaOpenAI APIClaude APIGemini APILangGraphAutoGenCrewAIMCP

⬇️ Download Syllabus 💬 Enquire About This Course

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Software Development

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YOUR MENTOR

Who teaches this course

Sessions are taught by someone who does this work, not by a full-time trainer reading slides.

👨‍🏫

Fourcup AI Faculty

Real Industry Trainers, Not Just Instructors

Every Fourcup trainer has a minimum of 8+ years of total IT industry experience and 5+ years of specific, hands-on experience in the exact tool or domain they teach. They are currently working professionals who use these GenAI tools in live projects, not instructors teaching from slides.

8+ years experience Active practitioner

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Software Development

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BATCHES

When you can start

Weekday, weekend and fast-track options. Seats are limited so sessions stay small enough to ask questions in.

Batch dates for this course have not been added yet.

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Soft Skills & Interview Readiness Add-On

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

FigmaReactTypeScriptNext.jsTailwind CSS+12 more

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FAQ

Questions people actually ask

If yours is not here, call us — a working engineer will answer, not a call centre.

No questions have been added for this course yet.

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Soft Skills & Interview Readiness Add-On

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Software Development

Front-End & UI/UX Development — React, Next.js & Design Systems

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