Generative AI Certification Course in Chandigarh — LLMs, LangChain & RAG

Verifiable Certificate ID

Each certificate carries a unique ID and QR code so employers can confirm it directly from our records.

Competencies Listed

Your certificate names the exact skills validated — LLMs, LangChain, Prompt Engineering, RAG, Fine-Tuning, Multimodal AI.

Capstone-Backed

Issued only after submission of a working LLM application capstone reviewed by our AI mentors, not on attendance alone.

Three Recognised Tiers

Fundamental (3 mo), Advanced (6 mo) or Pro + GenAI Engineering (12 mo) — pick the certification level that fits your goal.

Understand the landscape of generative AI — what it is, how it works, the difference between discriminative and generative models, and real-world applications transforming industries.

Master Python programming essentials — data types, functions, OOP, API calls, and key libraries needed for building generative AI applications.

Learn neural network fundamentals — perceptrons, activation functions, backpropagation, and training deep learning models with TensorFlow and PyTorch.

Master text processing, tokenization, word embeddings, sequence models, and the NLP foundations that underpin modern language models.

Understand the transformer architecture in detail — self-attention, multi-head attention, positional encoding, and why transformers revolutionized AI.

Explore LLM architectures including GPT, BERT, LLaMA, and Claude. Understand pre-training, fine-tuning, inference, and the capabilities and limitations of modern LLMs.

Master the art and science of prompt engineering — zero-shot, few-shot, chain-of-thought, and advanced prompting techniques for extracting maximum value from LLMs.

Build applications using OpenAI API, Claude API, and open-source LLM APIs. Learn API integration, token management, streaming, and building production-ready LLM applications.

Master LangChain for building complex AI applications — chains, agents, tools, memory, and output parsers. Build chatbots, document Q&A systems, and AI assistants.

Build RAG systems that combine LLMs with external knowledge bases. Learn vector databases, embeddings, chunking strategies, and retrieval optimization for accurate AI responses.

Fine-tune open-source LLMs on custom datasets. Learn LoRA, QLoRA, PEFT techniques, and build domain-specific AI models for specialized applications.