GenAI Full Stack Developer Master Program
An intensive 4-month online weekend live engineering cohort (Sat & Sun • 4 hours/day: 2h live faculty lectures + 2h supervised coding labs) covering GenAI Full Stack Developer production capstones and 580+ AI interviews, with eligible hiring-drive access under documented placement terms and tuition-refund protection where all policy conditions are met.
- Online Weekend Live Sessions (Sat & Sun • 4 Hours / Day)
- 4 Months (16 Weeks) Comprehensive Finish
- Guaranteed Placement Drives until Placed across 1,050+ top companies
- 26 Production Microservices & Capstones with GitHub code reviews
- 580+ Topic Mock Tests & 1-on-1 AI Voice Technical Interviews
Our Graduates Get Marketed to 1,050+ Global Tech Leaders & Unicorns
Continuous corporate interview referrals until job offer letter issuance:
Online Weekend Master Track: 4 Months of Live Mentorship & Placement Drives
Designed for college students and working professionals, our Online Weekend Master Track delivers intensive 4-hour live sessions every Saturday and Sunday across 16 weeks (4 Months). Complete 128+ hours of live faculty instruction, 26 production capstones, and 580+ AI interviews with unlimited corporate interview drives until you get placed!
Live Architecture & Core Mentorship
2 Hours of interactive enterprise architecture, live faculty coding, and design patterns followed by 2 Hours of supervised capstone development.
Hands-On Labs, Tests & AI Practice
2 Hours of advanced microservices, real-time queues & cloud deployment followed by 2 Hours of timed mock tests and 1-on-1 AI voice interview rounds.
4-Month Master Roadmap to Guaranteed Placement
We transform you into a battle-tested software engineer ready to clear Tier-1 technical and system design interview rounds in 4 months with weekend online sessions.
Architecture & Core Mechanics
Core OOP, memory mechanics, data structures, algorithms & clean design patterns.
Capstones & Microservices
Build production full stack SaaS, microservices, REST APIs, queues & cloud deployment.
System Design & AI Interviews
Simulate live FAANG interview rounds, timed topic mock tests and AI voice evaluations.
Corporate Drives until Placed
Resume marketing, hiring drives across 1,050+ partners, and placement guarantee.
Projected Target CTC After Program
Industry-verified compensation brackets achieved by graduates across 1,050+ hiring partners:
₹8.5L – ₹14L /yr
Software Engineer I, Junior Backend Developer, Full Stack Associate.
- 260+ Hours Live Mentorship
- 26 Capstone Projects on GitHub
- 580+ AI Technical Interview Scorecards
₹14L – ₹24L /yr
Full Stack Java Engineer, Spring Boot Microservices Specialist, Cloud Engineer.
- Kafka Event-Driven Architectures
- Redis Caching & Performance Tuning
- Docker, Kubernetes & AWS CI/CD
₹24L – ₹36L+ /yr
Senior Full Stack Engineer, Microservices Architect, Lead Consultant.
- High-Throughput System Design (LLD/HLD)
- Fault Tolerance & Distributed Transactions
- FAANG System Design Clearing Mentorship
Topic-Wise Curriculum & Practice Hub
34 Modules • 90 Deep-Dive Topics • 90 Integrated Topic Mock Tests & AI Interviews
Modules 1, 2, 3, 4: Modern Web Development, HTML5, CSS3, Tailwind CSS.
Modern Web Development
Topics: How the web works; Client/server architecture; Browser; HTTP; HTTPS; DNS; Domains; Hosting; Request-response cycle; Frontend vs backend; APIs; Databases; JSON; Cookies; Sessions; Authentication; Assignment: Browser → Frontend → API → Database → AI Model architecture.
- Explain the core concepts and architecture of Modern Web Development.
- Apply Modern Web Development in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HTML5
Topics: HTML structure; Semantic HTML; headings; paragraphs; links; images; forms; inputs; tables; lists; accessibility; metadata; SEO fundamentals; Mini Project: Developer Portfolio Page.
- Explain the core concepts and architecture of HTML5.
- Apply HTML5 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CSS3
Topics: CSS syntax; selectors; box model; typography; spacing; positioning; flexbox; grid; responsive design; animations; transitions; media queries; mobile-first development; Project: Responsive SaaS Landing Page.
- Explain the core concepts and architecture of CSS3.
- Apply CSS3 in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tailwind CSS
Topics: utility-first CSS; responsive utilities; layouts; forms; components; dark mode; design consistency; reusable patterns; Project: AI SaaS Landing Page.
- Explain the core concepts and architecture of Tailwind CSS.
- Apply Tailwind CSS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 5, 6, 7: JavaScript Fundamentals, Modern JavaScript, JavaScript Async Programming.
JavaScript Fundamentals
Topics: variables; let; const; primitive types; operators; functions; arrays; objects; conditions; loops; scope; closures; DOM; events; JSON
- Explain the core concepts and architecture of JavaScript Fundamentals.
- Apply JavaScript Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modern JavaScript
Topics: ES6+; destructuring; spread; rest; modules; template literals; arrow functions; map; filter; reduce; promises; async/await; fetch; API integration; error handling; Project: Weather/API Dashboard.
- Explain the core concepts and architecture of Modern JavaScript.
- Apply Modern JavaScript in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
JavaScript Async Programming
Critical for AI applications; Topics: synchronous vs asynchronous; callbacks; promises; Promise.all; async; await; error handling; API calls; concurrent requests; streaming concepts
- Explain the core concepts and architecture of JavaScript Async Programming.
- Apply JavaScript Async Programming in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 8: TypeScript.
TypeScript
Topics: Why TypeScript?; Primitive types; arrays; objects; union types; interfaces; type aliases; generics; enums; utility types; function typing; API response typing; type narrowing; reusable application models; Project: Convert JavaScript application to TypeScript
- Explain the core concepts and architecture of TypeScript.
- Apply TypeScript in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 9, 10, 11, 12, 13: React Fundamentals, React Hooks, Advanced React, State Management, React Form Engineering.
React Fundamentals
Topics: React architecture; JSX; Components; Props; State; Events; Conditional rendering; Lists; Forms; Component composition
- Explain the core concepts and architecture of React Fundamentals.
- Apply React Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
React Hooks
Topics: useState; useEffect; useRef; useMemo; useCallback; Context API; custom hooks; Assignment: Build reusable hooks for API calls
- Explain the core concepts and architecture of React Hooks.
- Apply React Hooks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced React
Topics: reusable components; component architecture; controlled forms; error boundaries concepts; loading states; optimistic UI concepts; pagination; infinite scroll; memoization; performance optimization
- Explain the core concepts and architecture of Advanced React.
- Apply Advanced React in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
State Management
Topics: Local state; Server state; Context; Zustand concepts; Redux Toolkit concepts; caching; application state design
- Explain the core concepts and architecture of State Management.
- Apply State Management in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
React Form Engineering
Topics: form validation; controlled forms; schema validation; file upload; dynamic fields; error messages; UX best practices; Mini Project: User Registration + Profile UI.
- Explain the core concepts and architecture of React Form Engineering.
- Apply React Form Engineering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 14, 15, 16, 17: Next.js Fundamentals, Server & Client Components, Next.js Data Fetching, SEO for Full Stack Developers.
Next.js Fundamentals
Topics: Why Next.js?; Project structure; App Router; layouts; pages; nested routes; dynamic routes; loading UI; error UI; metadata; navigation
- Explain the core concepts and architecture of Next.js Fundamentals.
- Apply Next.js Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Server & Client Components
Topics: Server Components; Client Components; rendering boundaries; data fetching; server-side execution; browser-side execution; application architecture
- Explain the core concepts and architecture of Server & Client Components.
- Apply Server & Client Components in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Next.js Data Fetching
Topics: server fetching; client fetching; caching concepts; revalidation; loading state; error state; pagination; search; filtering
- Explain the core concepts and architecture of Next.js Data Fetching.
- Apply Next.js Data Fetching in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
SEO for Full Stack Developers
Topics: metadata; title; description; canonical concepts; Open Graph; structured data; sitemap; robots; page performance
- Explain the core concepts and architecture of SEO for Full Stack Developers.
- Apply SEO for Full Stack Developers in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 18, 19, 20: Python Fundamentals, Advanced Python, Python for Production Applications.
Python Fundamentals
Topics: Syntax; variables; operators; strings; lists; tuples; sets; dictionaries; functions; modules; loops; conditions
- Explain the core concepts and architecture of Python Fundamentals.
- Apply Python Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced Python
Topics: OOP; exceptions; decorators; generators; iterators; comprehensions; files; JSON; typing; environments; packages
- Explain the core concepts and architecture of Advanced Python.
- Apply Advanced Python in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Python for Production Applications
Topics: environment variables; config management; logging; structured code; packages; dependency management; validation; async programming; testing concepts; Project: Python REST Client Application.
- Explain the core concepts and architecture of Python for Production Applications.
- Apply Python for Production Applications in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 21, 22, 23: SQL, Advanced SQL, PostgreSQL.
SQL
Topics: databases; tables; columns; primary keys; foreign keys; INSERT; SELECT; UPDATE; DELETE; WHERE; JOIN; GROUP BY; ORDER BY; aggregation; subqueries; indexes
- Explain the core concepts and architecture of SQL.
- Apply SQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced SQL
Topics: joins; CTE; window functions; transactions; normalization; indexing; query optimization; constraints; database design
- Explain the core concepts and architecture of Advanced SQL.
- Apply Advanced SQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PostgreSQL
Practical: Build databases for; users; roles; subscriptions; conversations; messages; documents; AI usage; projects
- Explain the core concepts and architecture of PostgreSQL.
- Apply PostgreSQL in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 24, 25, 26, 27, 28: FastAPI Fundamentals, Pydantic & Validation, FastAPI Architecture, Async FastAPI, ORM & Database Integration.
FastAPI Fundamentals
Topics: FastAPI architecture; routing; path parameters; query parameters; request bodies; response models; status codes; validation; Swagger/OpenAPI
- Explain the core concepts and architecture of FastAPI Fundamentals.
- Apply FastAPI Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Pydantic & Validation
Topics: schemas; models; nested models; validation; serializers; structured API contracts
- Explain the core concepts and architecture of Pydantic & Validation.
- Apply Pydantic & Validation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
FastAPI Architecture
Students learn structure such as; api/; models/; schemas/; services/; repositories/; core/; security/; ai/; tests/
- Explain the core concepts and architecture of FastAPI Architecture.
- Apply FastAPI Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Async FastAPI
Topics: async/await; concurrency; I/O; async database operations; external APIs; parallel AI calls; performance considerations
- Explain the core concepts and architecture of Async FastAPI.
- Apply Async FastAPI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
ORM & Database Integration
Topics: ORM concepts; models; relationships; repositories; transactions; migrations; query patterns; database sessions
- Explain the core concepts and architecture of ORM & Database Integration.
- Apply ORM & Database Integration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 29, 30, 31: REST API Design, API Documentation, External API Integration.
REST API Design
Topics: REST principles; resources; URI design; HTTP methods; status codes; pagination; filtering; sorting; search; API versioning; standardized errors
- Explain the core concepts and architecture of REST API Design.
- Apply REST API Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
API Documentation
Topics: OpenAPI; Swagger; API examples; request/response contracts; error documentation
- Explain the core concepts and architecture of API Documentation.
- Apply API Documentation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
External API Integration
Students integrate; AI APIs; email; payment concepts; storage; external services
- Explain the core concepts and architecture of External API Integration.
- Apply External API Integration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 32, 33, 34, 35: Authentication, Authorization, OAuth, Application Security.
Authentication
Topics: signup; login; password hashing; JWT; access tokens; refresh tokens; logout; session management
- Explain the core concepts and architecture of Authentication.
- Apply Authentication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Authorization
Topics: RBAC; users; admins; permissions; protected endpoints; ownership checks
- Explain the core concepts and architecture of Authorization.
- Apply Authorization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
OAuth
Concepts; Google login; GitHub login; authorization flows; identity providers
- Explain the core concepts and architecture of OAuth.
- Apply OAuth in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Application Security
Topics: OWASP awareness; CORS; CSRF concepts; XSS; SQL injection; authentication attacks; rate limiting; secure cookies; HTTPS; secret management; validation; secure headers
- Explain the core concepts and architecture of Application Security.
- Apply Application Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 36: Full Stack Integration.
Full Stack Integration
Connect Next.js → FastAPI → PostgreSQL; implement registration, login, dashboard, profile, CRUD, search, pagination and authentication; Major Project #1: Full Stack SaaS Application.
- Explain the core concepts and architecture of Full Stack Integration.
- Apply Full Stack Integration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 37, 38, 39: AI & GenAI Fundamentals, LLM Application Fundamentals, Prompt Engineering.
AI & GenAI Fundamentals
Topics: AI; ML; Deep Learning; Generative AI; Transformers; LLMs; tokens; context; inference; hallucinations
- Explain the core concepts and architecture of AI & GenAI Fundamentals.
- Apply AI & GenAI Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LLM Application Fundamentals
Topics: model APIs; messages; instructions; context; output; temperature concepts; token limits; structured output
- Explain the core concepts and architecture of LLM Application Fundamentals.
- Apply LLM Application Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Prompt Engineering
Topics: instructions; context; constraints; examples; few-shot prompting; structured prompts; JSON responses; prompt templates; prompt testing; context engineering
- Explain the core concepts and architecture of Prompt Engineering.
- Apply Prompt Engineering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 40, 41, 42: AI API Backend, AI Provider Abstraction, Streaming AI Responses.
AI API Backend
Students implement; POST /api/chat; POST /api/generate; POST /api/summarize; POST /api/extract; POST /api/classify
- Explain the core concepts and architecture of AI API Backend.
- Apply AI API Backend in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Provider Abstraction
Do not couple entire application to one provider; Architecture; AIProvider; OpenAIProvider; GeminiProvider; OtherProvider; Students learn; abstraction; configuration; model routing; fallback concepts
- Explain the core concepts and architecture of AI Provider Abstraction.
- Apply AI Provider Abstraction in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Streaming AI Responses
Critical AI full-stack topic; Backend: token streaming; async generators; SSE concepts; WebSockets concepts; Frontend: live response rendering; partial updates; stop generation; loading state; error handling; Project: Streaming AI Chat Application:
- Explain the core concepts and architecture of Streaming AI Responses.
- Apply Streaming AI Responses in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 43, 44: Building ChatGPT-Style Interfaces, Conversation Persistence.
Building ChatGPT-Style Interfaces
Features: chat sidebar; conversations; message history; markdown; code blocks; copy button; regenerate; edit prompt; model selector; responsive interface; streaming; stop response; feedback; timestamps
- Explain the core concepts and architecture of Building ChatGPT-Style Interfaces.
- Apply Building ChatGPT-Style Interfaces in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conversation Persistence
Database tables; conversations; messages; message_feedback; usage_logs; Implement; create conversation; rename conversation; delete conversation; message history; search history
- Explain the core concepts and architecture of Conversation Persistence.
- Apply Conversation Persistence in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 45, 46: File Upload, Document Processing.
File Upload
Support; PDF; DOCX; TXT; CSV; Topics; upload validation; file size; MIME type; secure filenames; storage; metadata
- Explain the core concepts and architecture of File Upload.
- Apply File Upload in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Document Processing
Pipeline: Upload → Parse → Clean → Chunk → Embed → Store.
- Explain the core concepts and architecture of Document Processing.
- Apply Document Processing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 47: Embeddings.
Embeddings
Topics: vectors; embeddings; semantic similarity; cosine similarity; embedding models; document embeddings; query embeddings
- Explain the core concepts and architecture of Embeddings.
- Apply Embeddings in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 48: Vector Search.
Vector Search
Hands-on; FAISS; pgvector; managed-vector-DB concepts; Topics: indexing; metadata; filtering; similarity; namespaces; retrieval
- Explain the core concepts and architecture of Vector Search.
- Apply Vector Search in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 49, 50, 51, 52: RAG Fundamentals, RAG Backend API, RAG Frontend, Advanced RAG.
RAG Fundamentals
Architecture; User; Question; Embedding; Vector Search; Relevant Context; LLM; Grounded Answer
- Explain the core concepts and architecture of RAG Fundamentals.
- Apply RAG Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RAG Backend API
Create; POST /documents/upload; POST /documents/index; POST /rag/query; GET /documents; DELETE /documents/{id}
- Explain the core concepts and architecture of RAG Backend API.
- Apply RAG Backend API in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RAG Frontend
Build; document library; upload progress; document status; document chat; citations; source viewer; answer feedback
- Explain the core concepts and architecture of RAG Frontend.
- Apply RAG Frontend in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced RAG
Topics: chunking; semantic chunking; metadata filtering; hybrid retrieval; query rewriting; reranking; multi-query; conversation-aware retrieval; citation generation; context compression; Major Project #2: Enterprise Document Intelligence Platform:
- Explain the core concepts and architecture of Advanced RAG.
- Apply Advanced RAG in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 53: LangChain.
LangChain
Topics: models; prompts; outputs; tools; retrievers; document loaders; vector stores; agents; middleware concepts
- Explain the core concepts and architecture of LangChain.
- Apply LangChain in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 54, 55, 56: Agentic AI, Tool Calling, Agent UI.
Agentic AI
Topics: Agent loop; tools; reasoning; actions; observations; state; memory; planning; permissions; human approvals
- Explain the core concepts and architecture of Agentic AI.
- Apply Agentic AI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Calling
Create tools for; calculator; user database; CRM; weather/API example; product search; internal services
- Explain the core concepts and architecture of Tool Calling.
- Apply Tool Calling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent UI
Build frontend displaying; user request; agent status; tool being used; task progress; completion; approvals; errors; Do not expose hidden reasoning; Display operational events instead
- Explain the core concepts and architecture of Agent UI.
- Apply Agent UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 57, 58, 59: LangGraph Fundamentals, Stateful AI Workflows, Multi-Agent Systems.
LangGraph Fundamentals
Topics: nodes; edges; state; routing; checkpoints; persistence; interrupts; human-in-the-loop; durable workflows
- Explain the core concepts and architecture of LangGraph Fundamentals.
- Apply LangGraph Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Stateful AI Workflows
Build: Input → Classifier → Research → Generator → Reviewer → Human Approval → Output.
- Explain the core concepts and architecture of Stateful AI Workflows.
- Apply Stateful AI Workflows in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Agent Systems
Agents; researcher; writer; reviewer; analyst; supervisor; Major Project #3: Multi-Agent Research Application:
- Explain the core concepts and architecture of Multi-Agent Systems.
- Apply Multi-Agent Systems in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 60: Model Context Protocol.
Model Context Protocol
Topics: MCP concepts; clients; servers; tools; resources; prompts; integrations; security; authorization concepts; Lab: Connect AI application with an MCP-enabled tool/service
- Explain the core concepts and architecture of Model Context Protocol.
- Apply Model Context Protocol in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 61, 62, 63: Vision, Audio AI, Multimodal Full Stack UI.
Vision
Build apps that understand; images; screenshots; documents; charts
- Explain the core concepts and architecture of Vision.
- Apply Vision in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Audio AI
Concepts; speech to text; text to speech; voice assistants; streaming audio concepts
- Explain the core concepts and architecture of Audio AI.
- Apply Audio AI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multimodal Full Stack UI
Create frontend handling; image upload; file preview; audio recording; multimodal prompts
- Explain the core concepts and architecture of Multimodal Full Stack UI.
- Apply Multimodal Full Stack UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 64, 65: Redis, AI Response Caching.
Redis
Topics: caching; key/value; TTL; session caching; response caching; rate-limiting concepts
- Explain the core concepts and architecture of Redis.
- Apply Redis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Response Caching
Topics: semantic caching concepts; prompt cache; conversation caching; cost optimization
- Explain the core concepts and architecture of AI Response Caching.
- Apply AI Response Caching in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 66, 67: Long-Running AI Jobs, Progress UI.
Long-Running AI Jobs
Examples; large document ingestion; report generation; batch embeddings; indexing; Learn; jobs; workers; job state; retry; progress tracking
- Explain the core concepts and architecture of Long-Running AI Jobs.
- Apply Long-Running AI Jobs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Progress UI
States; QUEUED; PROCESSING; COMPLETED; FAILED; Frontend shows real-time or polling-based job progress
- Explain the core concepts and architecture of Progress UI.
- Apply Progress UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 68, 69: GenAI Security, Multi-Tenant AI Security.
GenAI Security
Topics: prompt injection; indirect prompt injection; jailbreak awareness; malicious documents; data leakage; excessive tool permissions; output validation; system instruction exposure; secrets; user isolation
- Explain the core concepts and architecture of GenAI Security.
- Apply GenAI Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Tenant AI Security
Critical SaaS topic; Ensure; User A can never access; User B documents; User B conversations; User B vectors; User B files; Teach; tenant isolation; authorization; ownership validation
- Explain the core concepts and architecture of Multi-Tenant AI Security.
- Apply Multi-Tenant AI Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 70, 71: LLM Evaluation, RAG Evaluation.
LLM Evaluation
Measure; quality; relevance; correctness; groundedness; faithfulness; citation accuracy; latency; cost
- Explain the core concepts and architecture of LLM Evaluation.
- Apply LLM Evaluation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RAG Evaluation
Evaluate; retrieval quality; retrieved context; generated answer; unsupported answers
- Explain the core concepts and architecture of RAG Evaluation.
- Apply RAG Evaluation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 72, 73, 74, 75: Frontend Testing, Backend Testing, AI Application Testing, End-to-End Testing.
Frontend Testing
Topics: component tests; UI testing concepts; form testing; API mocking
- Explain the core concepts and architecture of Frontend Testing.
- Apply Frontend Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Backend Testing
Topics: unit testing; pytest; API testing; fixtures; mocking; database tests
- Explain the core concepts and architecture of Backend Testing.
- Apply Backend Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Application Testing
Topics: deterministic tests; golden datasets; prompt regression; RAG evaluation; tool tests; agent workflow testing
- Explain the core concepts and architecture of AI Application Testing.
- Apply AI Application Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
End-to-End Testing
Test the complete Login → Upload → Index → Ask → Generate → Save workflow.
- Explain the core concepts and architecture of End-to-End Testing.
- Apply End-to-End Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 76, 77: Logging, AI Observability.
Logging
Track; request IDs; user; endpoint; latency; errors; AI provider; model; token usage; Never log sensitive data unnecessarily
- Explain the core concepts and architecture of Logging.
- Apply Logging in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Observability
Dashboard metrics; AI requests/day; average response time; error rate; token consumption; cost; model distribution; RAG performance; failed tools
- Explain the core concepts and architecture of AI Observability.
- Apply AI Observability in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 78, 79, 80: Git & GitHub, Docker, CI/CD.
Git & GitHub
Topics: repositories; branches; commits; pull requests; conflict resolution; code review; releases
- Explain the core concepts and architecture of Git & GitHub.
- Apply Git & GitHub in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Docker
Containerize; Next.js; FastAPI; PostgreSQL; Redis; Using Docker Compose locally
- Explain the core concepts and architecture of Docker.
- Apply Docker in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
CI/CD
Pipeline: Commit → Test → Build → Security Check → Deploy.
- Explain the core concepts and architecture of CI/CD.
- Apply CI/CD in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 81, 82, 83: AWS Fundamentals, Production Deployment, Domain & HTTPS.
AWS Fundamentals
Services/Concepts: IAM; networking; EC2; S3; RDS; containers; monitoring; secrets
- Explain the core concepts and architecture of AWS Fundamentals.
- Apply AWS Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Production Deployment
Deploy; Frontend: Next.js application; Backend: FastAPI; Database: PostgreSQL; Files: Object storage; Cache: Redis; AI: External/provider AI services
- Explain the core concepts and architecture of Production Deployment.
- Apply Production Deployment in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Domain & HTTPS
Topics; domain; DNS; HTTPS; certificates; environment configuration
- Explain the core concepts and architecture of Domain & HTTPS.
- Apply Domain & HTTPS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 84, 85: Full Stack System Design, AI Architecture Patterns.
Full Stack System Design
Design; AI SaaS: Traffic; 100 users; 10,000 users; 1,000,000 users concepts; Discuss; scalability; caching; queues; database scaling; observability; fallback; reliability
- Explain the core concepts and architecture of Full Stack System Design.
- Apply Full Stack System Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AI Architecture Patterns
Architectures: Simple LLM app; Chat application; RAG; Agent; Multimodal system; Multi-agent platform; AI SaaS
- Explain the core concepts and architecture of AI Architecture Patterns.
- Apply AI Architecture Patterns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 86, 87: Token Economics, Cost Controls.
Token Economics
Topics; input tokens; output tokens; cached input; model choice; cost per user; monthly budgets; quotas
- Explain the core concepts and architecture of Token Economics.
- Apply Token Economics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cost Controls
Implement; user quotas; daily limits; subscription limits; rate limits; model routing; caching
- Explain the core concepts and architecture of Cost Controls.
- Apply Cost Controls in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Modules 88, 89, 90: Building AI SaaS, Plans & Usage, Usage Tracking.
Building AI SaaS
Features; landing page; registration; authentication; dashboard; subscription; AI workspace; conversation history; file storage; settings
- Explain the core concepts and architecture of Building AI SaaS.
- Apply Building AI SaaS in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Plans & Usage
Example; Free: Limited requests; Pro: Higher limits; Business: Team capabilities; Students learn architecture only
- Explain the core concepts and architecture of Plans & Usage.
- Apply Plans & Usage in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Usage Tracking
Track; requests; tokens; storage; documents; conversations; subscription limits; ===================================:
- Explain the core concepts and architecture of Usage Tracking.
- Apply Usage Tracking in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
26 Hands-On Production Capstones & Microservices
Build, deploy, and showcase real-world enterprise architectures on GitHub to prove your production engineering readiness:
Enterprise E-Commerce Microservices & Event-Driven Streaming Platform
Architected with Spring Boot 3.3, Apache Kafka event streams, Redis distributed caching, React 19 UI, and PostgreSQL. Features distributed ACID transaction sagas, payment webhook handling, dynamic inventory locking, and Dockerized Kubernetes deployment.
High-Throughput Banking & Core Transaction Engine
Concurrent multithreaded financial transaction ledger with ACID compliance, optimistic row locking, idempotent payment endpoints, and audit logging.
Real-Time Logistics & Fleet Tracking Service
Bi-directional live vehicle telemetry dashboard processing 10,000+ geo-coordinate events/sec with live map rendering and ETA calculations.
Multi-Tenant SaaS Subscription & Webhook Gateway
Multi-tenant automated billing engine with webhook signature verification, dynamic token bucket rate-limiting, and tenant data isolation schemas.
Distributed URL Shortener & Analytics System (Bitly Scale)
Low-latency URL redirection engine with distributed ID generation (Snowflake), sub-5ms Redis caching, and real-time click analytics.
Automated Cloud DevOps CI/CD Pipeline on AWS
Production containerization pipeline with automated testing, sonar code quality gates, container image vulnerability scanning, and zero-downtime rolling deploys.
AI-Powered Code Reviewer & Assessment Engine
Automated coding interview evaluator that parses Java AST trees, detects algorithmic time complexity, and simulates 1-on-1 voice technical interview feedback.
MockAttempt Academy vs. Traditional Bootcamps & Self-Study
Transparent side-by-side comparison of daily schedule, duration, curriculum, and placement support:
| Feature & Deliverables | MockAttempt Fast-Track Track | Expensive Bootcamps | Self-Study / YouTube |
|---|---|---|---|
| Live Weekend Schedule | Sat & Sun (4 Hours / Day) | 1 - 1.5 Hours / Day | Self-Paced / Inconsistent |
| Duration to Placement Readiness | 4 Months (16 Weeks Weekend) | 6 - 9 Months | 12+ Months (Uncertain) |
| Candidate Placement Guarantee | Unlimited Drives until Placed (or Full Refund) | Limited to 3-6 Months only | None (Apply blindly) |
| Topic Mock Tests & AI Interviews | Integrated for Every Topic (580+ Rounds) | End of Course Only | None |
| Tuition Fee | ₹49,999 ₹98,999 | ₹1,20,000 - ₹2,50,000 | Free (No Mentorship/Jobs) |
Institutional Course Assurances & 100% Placement Policy
100% Job Placement Guarantee
Our placement team arranges unlimited corporate interview drives across 1,050+ hiring partners until you receive an official offer letter. If unplaced, 100% of your tuition fee is refunded.
1-on-1 Expert Faculty Mentorship
Every cohort is taught live by seasoned lead architects from Tier-1 product companies with daily live coding and supervised code reviews.
Frequently Asked Questions
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