Course Objective: This curriculum provides a structured path from basic programming concepts to
production-ready AI applications. Students build real-world projects including web scrapers, chatbots,
RAG systems, custom models, and cloud deployments, developing both theoretical understanding and
practical implementation skills.
Course Structure
The curriculum is organized as a progressive 9-week journey, with each week introducing new concepts
while building on previous knowledge. Students complete hands-on projects that demonstrate mastery of
both fundamental principles and advanced techniques in language model engineering.
Introduction to language models through practical web scraping and analysis. Students learn to
interact with Google Gemini AI, implement robust web scraping pipelines, and apply cosine similarity
for semantic analysis. The project culminates in building an AI-powered research assistant capable
of summarizing web content with comprehensive error handling.
Topics: Gemini API Integration · Web Scraping · Cosine Similarity · Error Handling
Deep dive into recurrent neural networks and sequence modeling. Students implement RNN/LSTM
architectures from scratch, build interactive interfaces using Gradio, and explore function calling
capabilities. The week's project involves creating a context-aware airline assistance chatbot that
maintains conversation state across multiple interactions.
Topics: RNN/LSTM Architectures · Gradio UI Development · Tools and Function Calling ·
Conversation State Management
Understanding attention mechanisms and tokenization strategies. Students explore the transformer
attention mechanism, implement Byte-Pair Encoding (BPE) tokenization, and design structured user
interfaces for information extraction. The project focuses on building a system that extracts
actionable insights from meeting transcripts using BPE-tokenized input streams.
Topics: Attention Mechanisms · BPE Tokenization · Structured Output · Information Extraction
Comprehensive study of prompt engineering techniques and AI safety. Students master system
prompting, learn to steer model behavior effectively, and understand critical security concerns
including prompt injection attacks. This week emphasizes responsible AI development and robust
prompt design for production systems.
Topics: System Prompting · Model Steering · AI Safety · Prompt Injection Prevention
Introduction to Retrieval-Augmented Generation systems. Students learn various document chunking
strategies, implement vector databases using ChromaDB and FAISS, and deploy persistent searchable
knowledge bases. The project demonstrates how to build scalable RAG systems that efficiently
retrieve and utilize external knowledge.
Topics: Chunking Strategies · Vector Databases · ChromaDB/FAISS · Persistent Storage ·
Semantic Search
Running large language models locally without internet dependency. Students work with Ollama for
local model deployment, understand quantization techniques for model compression, and create custom
Modelfiles. This week emphasizes privacy-preserving AI and edge deployment scenarios where models
operate entirely offline.
Topics: Ollama · Model Quantization · Modelfiles · Edge Deployment · Privacy-Preserving AI
Advanced exploration of autonomous AI agents. Students implement self-reflective agents using
LangGraph, design agent workflows with decision-making loops, and build systems capable of planning,
acting, and learning from feedback. The project showcases agents that can reason about their own
outputs and iteratively improve their responses.
Topics: LangGraph · Self-Reflective Agents · Agent Workflows · Reasoning Loops · Autonomous
Decision-Making
Systematic evaluation and monitoring of AI systems. Students implement telemetry for cost and token
tracking, learn RAG evaluation using the Triad framework (context relevance, groundedness, answer
relevance), and quantify model performance metrics. This week focuses on measuring and optimizing
the ROI of AI applications in production environments.
Topics: Telemetry and Monitoring · Cost/Token Tracking · RAG Triad Evaluation · Performance
Metrics · ROI Analysis
Culminating project deploying a production-ready autonomous researcher agent. Students build
complete applications using FastAPI, implement Human-in-the-Loop (HITL) workflows for quality
assurance, and handle deployment considerations including API design, error handling, and
scalability. The capstone integrates all previous concepts into a fully functional, deployable AI
system.
Topics: FastAPI · Production Deployment · Human-in-the-Loop · API Design · System Integration
Course Credits
Prepared By:
Nilesh Sarkar, 5th Semester Student
Department of Artificial Intelligence and Robotics Engineering
Dayananda Sagar University, Bangalore
Under the Guidance of:
Dr. Pramod Kumar Naik, Chairperson
Department of Artificial Intelligence and Robotics Engineering
Dayananda Sagar University, Bangalore