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Techcadd
#1 Generative AI Training Institute in Mohali & Chandigarh

Master Generative AI
Build AI Apps, Agents & LLM Solutions for Real Careers

Join Techcadd Mohali's industry-focused Generative AI Course in Mohali & Chandigarh and learn Prompt Engineering, AI Agents, ChatGPT, Gemini, Claude, LangChain, RAG, Python, AI Automation and LLM Development through practical live projects, expert mentorship and placement assistance.

Live AI Projects
Industry Experts
Placement Assistance
AI Certification
Book Free Demo Class Download Syllabus

500+

Students Trained

40+

AI Projects

100%

Hands-on Learning
AI Assistant Dashboard
AI Assistant

Create an AI chatbot using RAG and LangChain.

AI Agent
LLM
Vector DB
LangChain
Model Training
AI Automation Workflow Automation
Prompt Engineering Advanced Prompting
Cloud Deployment Deploy AI Apps

Generative AI Course Details at a Glance

A career-focused Generative AI course in Mohali & Chandigarh built for freshers, developers, and working professionals — no advanced math or prior AI background required. New to coding altogether? Pair it with our Python Programming Course for a head start before you enrol.

Duration

3 to 6 months, with fast-track and weekend-only tracks available

Mode of Learning

Classroom training at our Mohali & Chandigarh centers, plus live instructor-led online batches

Eligibility

Class 12 pass-outs, graduates, IT professionals and career switchers — beginner friendly

Batch Options

Weekday, weekend and evening batches, kept small for hands-on mentorship

Certification

Industry-recognized Techcadd Generative AI & Prompt Engineering Certificate on completion

Fees

Affordable fee structure with easy EMI/installment options — talk to our counsellor for the current fee sheet

Learning Curriculum

Generative AI Course Tracks Built For The Future

Master targeted engineering skills or complete the entire sequence for a comprehensive GenAI specialization.

Generative AI Model Foundations

Demystify how large foundation systems function under the hood. Learn to evaluate pre-trained models and engineer structured training inputs for varied analytical text and token distributions.

  • Transformer neural network architectures & self-attention
  • Tokenization, embedding spaces, and context window dynamics
  • Dataset synthesis, parsing, and automated data cleaning
  • Evaluating model benchmarks (MMLU, HumanEval metrics)
  • Mitigating algorithmic bias and structural hallucination vectors
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AI Consultation & Solution Strategy

Develop the strategic skills needed to audit corporate operations, identify systemic inefficiencies, and construct high-yield AI implementation roadmaps for businesses.

  • Operational workflow discovery & business automation mapping
  • AI vs. traditional software cost-benefit financial evaluation
  • Selecting optimal open-source vs. proprietary system tech stacks
  • Data privacy standards, corporate GDPR alignment, and compliance
  • Structuring corporate proof-of-concept (PoC) validation frameworks
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Custom LLM Application Engineering

Program dynamic applications that interface directly with foundation models. Program clean system prompts, conversational state memory, and reliable programmatic outputs.

  • Advanced prompt engineering patterns (Few-Shot, CoT prompting)
  • Managing complex application state and conversational histories
  • API integration orchestration (OpenAI, Anthropic, local Ollama nodes)
  • Enforcing strictly structured computational data outputs via JSON schemas
  • Unit-testing prompt resilience against prompt injections & jailbreaks
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Retrieval-Augmented Generation (RAG) Systems

Connect models to internal knowledge repositories. Build systems capable of serving highly localized, source-backed answers from unstructured documents and company databases.

  • Document parsing, chunking, and overlapping window strategies
  • Generating vector embeddings and modern semantic search pipelines
  • Managing production vector stores (Pinecone, ChromaDB, PGVector)
  • Optimizing retrieval using Hybrid Search and Re-ranking algorithms
  • Evaluating production RAG precision via quantitative frameworks (Ragas)
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Autonomous AI Agent Architecture

Build multi-agent frameworks capable of breaking down complex objectives into sequential tool executions, verifying intermediate output accuracy autonomously.

  • Agent loop planning methodologies & standard ReAct frameworks
  • Binding executable tools, web-search APIs, and native database scripts
  • Multi-agent communication orchestration via LangGraph or CrewAI
  • Designing strict loop-prevention policies and task execution timeouts
  • State persistence, human-in-the-loop gates, and deep debugging
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Enterprise AI Workflow Automation

Integrate AI pipelines into standard productivity loops. Replace manual data manipulation with automated intelligent parsing, categorization, and routing mechanisms.

  • Automated semantic inbox parsing and contextual reply synthesis
  • Bulk pipeline document abstraction and key entity data extraction
  • Configuring production-grade multi-step triggers using Make and n8n
  • Streaming real-time synthesized database reports directly to teams
  • Building programmatic system logs and alert intervention thresholds
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Advanced Prompting & Multimodal AI

Leverage generative models across visual media and natural language processing layers to build composite multi-step marketing engines and creative assets.

  • Structuring unified, data-driven omni-channel copy generators
  • Configuring deterministic image production (Midjourney & Stable Diffusion)
  • Controlling visual composition using seed values and ControlNet layers
  • Scripting, programmatic voice cloning, and video asset sequencing
  • Enforcing strict asset compliance and brand visual requirements
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Production Architecture & UI Integration

Package experimental scripts into enterprise-ready web apps. Build beautiful frontend interfaces, implement API routing, and track operational metrics.

  • Building modular application frontends with React or Streamlit UIs
  • Developing high-performance streaming backend routes via FastAPI
  • Containerizing application architecture environments securely using Docker
  • Monitoring active tokens, production latencies, and runtime overhead costs
  • Implementing production rate-limiting rules and secure API validation
Explore Full Module
Why Techcadd

Architecting High-Performance GenAI Careers

We skip the generic text-only slide decks. Our training deployment ecosystem is engineered to translate absolute foundation theory into production-ready software engineering portfolios.

Elite Production Mentorship

Learn directly from active full-stack developers and automation engineers who build, ship, and debug LLM applications for real industry clients daily.

Production-Grade Portfolios

Graduate with verifiable repositories. Build, optimize, and deploy functioning RAG applications, vector search indexes, and multi-agent systems from scratch.

Sandbox Hands-On Labs

Write raw code immediately. Interface with live models via Jupyter Notebooks, configure API tokens, manage context sizes, and optimize inference latencies natively.

Aggressive Career Acceleration

Get custom portfolio reviews, aggressive interview preparation drills, programmatic ATS resume optimization, and placement ecosystem routing.

Responsible Architecture

Master enterprise data safety. Learn to handle secure user authentication, avoid systemic prompt injections, and implement toxic evaluation guardrails.

Adaptive Learning Tracks

Choose flexible batches tailored specifically to align perfectly around final-year college exams, technical university obligations, or corporate work hours.

Job-Validated Credentials

Earn an industry course certification that bypasses generic theory, validating your true ability to engineer real-world, scalable GenAI solutions.

Infinite Dev Network

Gain continuous, lifetime developer assistance channel access to consult on live startup pipelines, client project roadblocks, or future architectural upskilling paths.

Technology Stack

Production Toolkits & Frameworks

Techcadd focuses on modern, production-ready developer stacks that students can confidently build with and explain in technical interviews.

LLM Fine-Tuning
LangChain Framework
LlamaIndex
CrewAI Multi-Agents
Prompt Engineering
RAG Pipelines
Model Evaluation
Guardrails / Safety
Python Core
FastAPI
PHP Integration
RESTful APIs
Node.js Basics
Data Parsing
React JS
Next.js Framework
Tailwind CSS
Streamlit UIs
Responsive UI/UX
Shadcn/ui Concepts
Pinecone DB
ChromaDB
PGVector / Postgres
Firebase Firestore
Document Chunking
Semantic Search
Docker Containers
AWS Bedrock / S3
Hugging Face Spaces
GitHub Actions CI/CD
API Token Security
Cost Optimization
Project Outcomes

Production-Ready Portfolio Deployments

Don't just write scripts—build end-to-end applications complete with vector scaling, custom UI components, and real-time inference handling.

AI assistant project
LangChain FastAPI

Autonomous Course Orchestrator

An enterprise-grade chatbot system that parses curriculum dynamics, operational fee logic, and batch matrices from structural knowledge bases with sub-100ms streaming text responses.

View Architecture
RAG project
Pinecone PGVector

Enterprise Knowledge RAG Engine

A production document abstraction platform allowing deep semantic query lookups against uploaded PDFs, parsing heavy documentation sheets into hyper-contextualized, source-aware answers.

View Architecture
AI automation project
n8n Flows Claude 3.5

Multi-Modal Agency Workflow

An automated intelligence loop linking marketing copy generation engines, targeted social automation webhooks, and programmatic performance analysis logs inside single human-in-the-loop validation dashboards.

View Architecture
Start Your GenAI Journey

Accelerate Your Developer Potential

Connect with the Techcadd Mohali engineering panel to map a precise training matrix customized around your technical background, career milestones, and schedule.

Book Free Counselling
Got Questions?

Frequently Asked Questions

What exactly is covered in this Generative AI Course?

The curriculum spans advanced prompt engineering and multi-modal content creation (text, image, audio, video) through to building autonomous multi-agent systems and custom RAG pipelines connected to real developer APIs.

Do I need a strong coding or computer science background?

No prior coding experience is mandatory. We scale from absolute fundamentals through to no-code AI app generation using tools like v0.dev, then introduce Python-based automation step by step.

How does this course benefit enterprise automation?

You will learn to configure custom AI-driven workflows that cut down manual processing time, optimize inference token usage, and safely structure automated data parsing pipelines for real business operations.

Is there job placement assistance provided after graduation?

Yes, Techcadd maintains direct hiring-partner connections across Mohali and Chandigarh. Graduates receive portfolio reviews, mock technical interview practice, and dedicated placement support.

Which specific AI models will we gain hands-on access to?

You will work with industry-leading foundation models including OpenAI's GPT, Anthropic's Claude, Google Gemini, DeepSeek, and open-weight models like Meta's Llama.

Will I learn how to protect AI systems from prompt injections?

Yes, AI security is built into the curriculum. You will study guardrail patterns, build validation filters, and implement defensive wrappers to catch prompt-injection and jailbreak attempts.

Will we build real-world production projects during class?

Absolutely. The training is completely practical and lab-based. You will build and deploy real projects, including custom knowledge-base chatbots, prompt-injection filters, and deployed application wrappers.

What is the difference between generic AI tools and enterprise RAG?

Generic AI tools respond from general training data alone, whereas Retrieval-Augmented Generation (RAG) connects a model to your own vector database so answers are grounded in your private, real-time documents.

Can existing software products be integrated with AI workflows?

Yes. We cover API integration in depth, showing you exactly how to add LLM-powered logic and autonomous tool-calling into existing databases and running web applications.

How are the multi-modal AI media modules structured?

You will work with image generation tools like Midjourney and Stable Diffusion, video generation pipelines such as Runway, and AI audio/music tools like Suno for end-to-end multi-modal projects.

What kind of certificate will I receive upon completion?

You will receive an industry-recognized Generative AI & Prompt Engineering Certificate from Techcadd, validating your practical ability to build and deploy GenAI applications.

Is there post-course lab access or support?

Yes, students get continued access to our sandbox labs at the Mohali & Chandigarh centers along with a dedicated community channel for ongoing project help after the course ends.

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