Sure. For a **B.Tech 4th-year course**, I’d suggest a syllabus that balances **LLM fundamentals, Generative AI, hands-on implementation, and current industry practices** without going too deep into research-level mathematics. ## Tentative Syllabus: Generative AI and Large Language Models **Course:** Generative AI and Large Language Models **Level:** B.Tech 4th Year **Suggested Credits:** 3–4 **Prerequisites:** Python, basic Data Structures, Probability & Statistics, Machine Learning, Neural Networks/Deep Learning ### Unit 1 — Foundations of Generative AI - Introduction to Artificial Intelligence, Machine Learning and Deep Learning - Discriminative vs. generative models - What is Generative AI? - Generative AI applications and industry use cases - Overview of generative models - Autoencoders - Variational Autoencoders (VAEs) - Generative Adversarial Networks (GANs) - Autoregressive models - Diffusion models - Transformers - Generative AI ecosystem and model families - Challenges: hallucination, bias, privacy, copyright and safety **Practical:** Build a simple text-generation model using Python. --- ### Unit 2 — Neural Networks and Transformer Architecture - Neural-network fundamentals relevant to LLMs - Sequence modeling - RNNs, LSTMs and their limitations - Attention mechanism - Self-attention and cross-attention - Positional encoding - Transformer architecture - Encoder, decoder and encoder-decoder architectures - Transformer-based models: - BERT - GPT - T5 - Parameters, layers, context window and computational complexity **Practical:** Implement a simplified self-attention mechanism and explore a pretrained Transformer. --- ### Unit 3 — Large Language Models - What is an LLM? - LLM training pipeline - Pre-training and next-token prediction - Tokenization - Word-level - Subword - BPE - SentencePiece - Embeddings - Vocabulary and token IDs - Context windows - Scaling laws and model size - Inference and decoding - Greedy decoding - Beam search - Temperature - Top-k - Top-p - Overview of open and proprietary LLMs - LLM capabilities and limitations **Practical:** Use a pretrained LLM through a Python library/API and experiment with different decoding parameters. --- ### Unit 4 — Prompt Engineering and LLM Application Development - Introduction to prompt engineering - Zero-shot prompting - Few-shot prompting - Role/system prompting - Chain-of-thought concepts - Structured output - Prompt templates - Prompt evaluation - Function/tool calling - LLM APIs - Building conversational applications - Chatbots and AI assistants - Introduction to LLM application frameworks - Managing context and conversation history **Practical projects:** - Build a domain-specific chatbot - Build an LLM-based document summarizer - Build an AI assistant with tool/function calling --- ### Unit 5 — Retrieval-Augmented Generation (RAG) - Why LLMs hallucinate - Knowledge limitations of LLMs - RAG architecture - Document ingestion - Text cleaning and chunking - Embeddings and semantic search - Vector databases - Retrieval strategies - Context construction - Generation using retrieved information - Hybrid search - RAG evaluation - Common RAG failure modes - Advanced RAG concepts **Practical:** Build a **RAG chatbot over college/university documents**, PDFs or technical documentation. --- ### Unit 6 — Fine-Tuning and Adaptation of LLMs - Prompting vs. fine-tuning vs. RAG - Supervised fine-tuning - Instruction tuning - Dataset preparation - Training data formats - Parameter-efficient fine-tuning - LoRA - QLoRA - Adapters - Quantization - Model merging — overview - Fine-tuning evaluation - Catastrophic forgetting - When **not** to fine-tune **Practical:** Fine-tune a small open-source language model using a small domain-specific dataset. --- ### Unit 7 — Multimodal and Generative Models - Text-to-image generation - Diffusion models - Image embeddings - Vision-language models - Multimodal LLMs - Image-to-text and text-to-image systems - Speech and audio generation - Text-to-speech and speech-to-text - Multimodal applications - Introduction to video generation **Practical:** Develop a simple multimodal application, such as an image-question-answering system. --- ### Unit 8 — LLM Agents and Tool Use - What is an AI agent? - LLM as a reasoning/planning engine - Agent architecture - Tools and function calling - Memory - Planning and task decomposition - Retrieval tools - Web/API tools - Multi-step workflows - Single-agent vs. multi-agent systems - Agent evaluation and reliability - Agent safety **Practical:** Build an agent that can interact with multiple tools—for example, a research or data-analysis assistant. --- ### Unit 9 — Evaluation, Safety and Responsible AI - Why evaluating LLMs is difficult - Traditional NLP metrics - LLM-based evaluation - Human evaluation - Accuracy, relevance and groundedness - Hallucination evaluation - Bias and fairness - Prompt injection - Jailbreaking - Data privacy - Copyright and intellectual property - AI-generated misinformation - Responsible AI principles - Security considerations for LLM applications **Practical:** Evaluate a RAG/LLM application using a defined evaluation dataset and metrics. --- ### Unit 10 — LLM Deployment and Industry Applications - LLM inference - Model compression - Quantization - CPU vs. GPU inference - Serving LLMs - Latency and throughput - Cost optimization - Cloud vs. local deployment - LLMOps - Monitoring and logging - Scaling GenAI applications - Enterprise applications - Case studies: - Healthcare - Finance - Education - Software engineering - Customer support - Legal/knowledge systems **Practical:** Deploy an LLM/RAG application as a web service. --- ## Suggested Laboratory Component A good 4th-year course should be **at least 40–50% hands-on**. | Lab | Experiment | | --- | --- | | 1 | Introduction to Hugging Face/LLM ecosystem | | 2 | Tokenization and embeddings | | 3 | Implement self-attention | | 4 | Use a pretrained LLM | | 5 | Prompt engineering experiments | | 6 | Build an LLM chatbot | | 7 | Build a PDF/document Q&A system | | 8 | Implement RAG with a vector database | | 9 | Fine-tune a small LLM using LoRA/QLoRA | | 10 | Build an image/text multimodal application | | 11 | Build an LLM agent with tools | | 12 | Evaluate hallucination and RAG quality | | 13 | Deploy an LLM application | | 14 | Capstone project | ## Suggested Tools/Technologies Students could get exposure to: - **Python** - **PyTorch** - **Hugging Face Transformers** - **Hugging Face Datasets** - **Jupyter/Google Colab** - **LLM APIs** - **Embedding models** - **Vector databases** - **LangChain or LlamaIndex** - **FastAPI/Streamlit** - **Git/GitHub** - Basic cloud/GPU usage I would **not** make students memorize specific commercial models or frameworks, because those change rapidly. The syllabus should teach the underlying concepts and then use current tools for implementation. ## Suggested Assessment - **Internal/continuous assessment:** 30% - **Laboratory:** 20% - **Mid-semester examination:** 15% - **End-semester examination:** 25% - **Capstone/project:** 10% ### Capstone Project Ideas - College information RAG chatbot - Research-paper assistant - AI coding assistant - Resume analysis and job-matching system - Medical-literature research assistant - Legal-document Q&A system - Personalized learning assistant - Multimodal educational tutor - Financial-document analysis assistant - University examination/question-generation system ### One important recommendation For **B.Tech 4th year**, I would structure the course around this progression: **ML/DL → Transformers → LLMs → Prompting → RAG → Fine-tuning → Agents → Multimodal AI → Evaluation/Safety → Deployment** That gives students both the **theoretical foundation** needed for an engineering degree and the **practical skills** currently expected for GenAI/LLM roles.