Best Full Stack AI Training in Mohali, Chandigarh & Gurdaspur: Build MERN-Powered Intelligent Applications
Step into modern full-stack development and artificial intelligence with the comprehensive Full Stack AI Training program at Future Finders. Designed for software engineers, web developers, students, and tech professionals, this course combines MERN Stack development with practical generative AI integration.
Whether you are looking for Full Stack AI training in Mohali, Chandigarh, or Gurdaspur, hands-on classroom cohorts and instructor-led online sessions help you build robust backends, responsive frontends, and AI-enabled applications.
You will progress from MongoDB, Express.js, React, and Node.js to LLM APIs, open-source models, vector databases, LangChain, LlamaIndex, and model deployment concepts. Learn how to connect applications to data, design RAG workflows, build tool-using agents, and deploy full-stack systems. Career mentoring and placement assistance support preparation for roles such as Full Stack AI Engineer, MERN AI Developer, or AI Solutions Architect.
What is Full Stack AI (MERN Stack + Generative AI)?
- MERN Stack Architecture: Use MongoDB, Express.js, React, and Node.js to build complete web applications.
- Transformer Architectures & LLMs: Connect application backends to language models for context-aware features.
- Retrieval-Augmented Generation (RAG): Connect documents and databases to applications for source-grounded answers.
- Autonomous AI Agents & Tool Calling: Build workflows that can call APIs, query databases, and perform multi-step tasks.
Core Advantages of a Career in Full Stack AI
- AI-native application development: Combine complete web application development with generative AI integration.
- Broader career pathways: Build skills relevant to full-stack engineering, AI product engineering, and intelligent automation.
Learn from Proven Full Stack & AI Practitioners
- Practical full-stack and AI learning: Training focuses on secure web platforms, AI pipelines, application integration, and performance optimization.
Specialized Allied Technology Modules
- Advanced JavaScript & Node.js Backend: Asynchronous programming, REST APIs, JWT/OAuth authentication, and scalable servers.
- React & Modern Frontend Engineering: React hooks, state management, Tailwind CSS, and component-driven development.
- Vector Databases & Semantic Search: Explore Pinecone, ChromaDB, and Weaviate for similarity search.
- DevOps & Cloud Deployment: Docker, CI/CD, AWS, Vercel, and Hugging Face Spaces.
- AI Safety & Guardrails: Validation, filters, evaluation, and measures to reduce data leakage and unsafe outputs.
Core Technical Pillars & Architecture
- MERN Core Integration: MongoDB schemas, Express/Node REST and GraphQL endpoints, and React frontends.
- Prompt Engineering & Context Optimization: Zero-shot and few-shot prompting and context design.
- Building with LangChain & LlamaIndex: Create pipelines connecting models, chains, memory, and data indexers.
- Advanced RAG in Full-Stack Apps: Document chunking, hybrid search, and intelligent search/document assistants.
- Autonomous Agents & Function Calling: Explore agent workflows using LangGraph or CrewAI with computational and retrieval tools.
- Open-Source Models & Local Deployment: Run and serve models such as Llama and Mistral with tools including Ollama and vLLM.
Advanced Full Stack AI Engineering: Practical Industry Focus
- Real-World Capstone Projects: AI SaaS platforms, recommendation engines, customer-support copilots, and document search portals.
- Private & Secure Architecture First: Design privacy-aware architectures that protect company information.
- Modern Toolchain Mastery: React, Node.js, MongoDB, Express, LangChain, Ollama, and vector databases.
Dedicated Career Support: Mock interviews, GitHub portfolio reviews, system-design discussions, and placement-drive preparation.
Agentic AI Course
- Day 1: Python refresher, virtual environments → Setup, small scripts
- Day 2: Python functions, OOP, JSON → Parse JSON data
- Day 3: HTTP, APIs, async Python → Call a public API
- Day 4: What is Generative AI, LLMs, tokens, context → Explore chat models
- Day 5: Prompt engineering basics → Prompt templates for 5 tasks
- Day 6: Advanced prompting: few-shot, CoT, role prompts → Prompt library
- Day 7: LLM APIs (Claude/OpenAI) → First API chatbot in Python
- Day 8: Streaming, system prompts, parameters → CLI chatbot with streaming
- Day 9: Structured outputs, JSON schema, Pydantic → Resume parser
- Day 10: Mini Project 1 → Document summarizer tool
- Day 11: Tool/function calling → Calculator and weather tools
- Day 12: Building a basic agent loop (ReAct) → Agent from scratch
- Day 13: Embeddings and semantic search → Embed and search sentences
- Day 14: Vector databases (Chroma/FAISS) → Store and query documents
- Day 15: Document loading, chunking strategies → PDF ingestion pipeline
- Day 16: RAG pipeline → Chat with your PDF
- Day 17: Advanced RAG: reranking, hybrid search → Improve retrieval quality
- Day 18: Mini Project 2 → Company FAQ RAG bot
- Day 19: LangChain basics: chains, prompts, parsers → LCEL chains
- Day 20: LangChain tools and agents → Web search agent
- Day 21: Memory: short-term, long-term, summaries → Chatbot with memory
- Day 22: LangGraph fundamentals: nodes, edges, state → Simple graph workflow
- Day 23: LangGraph: conditional edges, loops → Self-correcting agent
- Day 24: Human-in-the-loop, checkpoints → Approval workflow agent
- Day 25: Planning and reflection patterns → Plan-and-execute agent
- Day 26: Multi-agent concepts → Researcher + writer agents
- Day 27: CrewAI → Content creation crew
- Day 28: AutoGen / OpenAI Agents SDK → Multi-agent conversation
- Day 29: Model Context Protocol (MCP) → Build a simple MCP server
- Day 30: MCP clients and tool integration → Connect agent to MCP tools
- Day 31: Browser/computer-use and code-execution agents → Web scraping agent
- Day 32: Agent for data: SQL and CSV agents → Natural-language data query bot
- Day 33: Evaluation of LLM apps (RAGAS, LangSmith) → Evaluate RAG bot
- Day 34: Guardrails, safety, prompt injection → Add input/output filters
- Day 35: Capstone planning → Architecture and tool design
- Day 36: FastAPI backend for agents → Expose agent as API
- Day 37: Frontend with Streamlit/Gradio → Chat UI for agent
- Day 38: Capstone: core agent logic → Tools + planning
- Day 39: Capstone: RAG + memory → Knowledge layer
- Day 40: Capstone: multi-agent orchestration → Final workflow
- Day 41: Docker and deployment → Containerize app
- Day 42: Cloud deployment (Render/AWS/HF Spaces) → Live deployment
- Day 43: Observability, cost control, caching → Add logging and token tracking
- Day 44: GitHub, README, demo video, resume → Portfolio package
Day 45: Final demo and viva → Present capstone
- Week 1: Python essentials, APIs, async → Scripts and API calls
- Week 2: LLM fundamentals, prompt engineering → Prompt library
- Week 3: LLM APIs, streaming, structured outputs → Chatbot + resume parser
- Week 4: Tool calling and ReAct agent → Agent from scratch
- Week 5: Embeddings, vector DBs → Semantic search engine
- Week 6: RAG pipeline and advanced retrieval → Chat with PDFs
- Week 7: LangChain: chains, tools, memory → Agent with web search + memory
- Week 8: LangGraph: stateful workflows → Self-correcting, human-in-loop agent
- Week 9: Multi-agent: CrewAI, AutoGen, Agents SDK → Research crew
- Week 10: MCP, browser and code agents → MCP server + web agent
- Week 11: Evaluation, guardrails, FastAPI, Streamlit → Evaluated, secured app
Week 12: Capstone, Docker, cloud deploy → Deployed multi-agent product, demo
- Week 1: Python fundamentals → Practice scripts
- Week 2: Python OOP, files, JSON, virtual envs → CLI utilities
- Week 3: APIs, async, Git/GitHub → API client project
- Week 4: ML and LLM basics, transformers overview → Explore Hugging Face models
- Week 5: Prompt engineering (basic to advanced) → Prompt evaluation sheet
- Week 6: LLM APIs, streaming, parameters → Streaming chatbot
- Week 7: Structured outputs, Pydantic, function calling → Data-extraction tool
- Week 8: ReAct and agent loop from scratch → Custom tool-using agent
- Week 9: Embeddings, similarity search → Semantic search app
- Week 10: Vector DBs: Chroma, FAISS, Pinecone → Multi-doc search
- Week 11: RAG fundamentals → PDF Q&A bot
- Week 12: Advanced RAG: hybrid, reranking, query rewriting, GraphRAG → Improve a RAG system
- Week 13: LangChain deep dive → Multi-tool agent
- Week 14: Memory systems → Personalized assistant
- Week 15: LangGraph core → Workflow agent
- Week 16: LangGraph advanced: checkpoints, human-in-loop, subgraphs → Approval-based agent
- Week 17: Planning, reflection, self-critique patterns → Coding/research agent
- Week 18: Multi-agent: CrewAI, AutoGen, Agents SDK → Content/research crew
- Week 19: MCP servers and clients → Custom MCP tools
- Week 20: Browser, computer-use, SQL and data agents → Web automation + data bot
- Week 21: Fine-tuning basics, open-source models (Ollama, local LLMs) → Run local agent
- Week 22: Evaluation, guardrails, security, observability → Eval dashboard
- Week 23: FastAPI, Streamlit/React UI, Docker, cloud deploy → Deployed agent app
Week 24: Final capstone and portfolio → Production-grade agentic product, demo day
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