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Course: LLM Foundational Course Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Total Marks: 100 Objective The objective of this assignment is to help you: Implement conversation memory that manages context windows Control LLM output using temperature, max_tokens, and stop sequences Build a complete agent that combines conversation history with semantic search Handle multi-turn conversations with proper context management Track usage and costs

Course: LLM Foundational Course Level: Medium Type: Individual Assignment Duration: 5–7 days Total Marks: 100 Objective The objective of this assignment is to help you: Understand tokenization and how it affects API costs Implement token counting and cost calculation functions Build a vector database from scratch using embeddings Perform semantic search to retrieve relevant documents Create a simple RAG system that answers questions using retrieved context Think pra

Course: AI Backend Engineering with FastAPI Assignment Type: Applied Project Assignment Difficulty Level: Medium → Advanced Estimated Time: 12–18 hours Submission Mode: Online LMS (Moodle / Canvas / Blackboard) Assignment Context Modern AI systems rarely consist of a single model endpoint. Instead, they involve multiple components working together such as: API orchestration model inference retrieval systems asynchronous processing background workflows logging and mon

Course: AI Backend Engineering with FastAPI Assignment Type: Capstone Implementation + Architecture Report Difficulty: Medium → Advanced Estimated Effort: 10–15 hours Submission Platform: LMS (Moodle / Canvas / Blackboard) Assignment Overview In this assignment, you will design and implement a production-style AI backend API using the concepts introduced throughout this course. You will build a modular FastAPI system that integrates: LLM inference Retrieval-Augmente

Course: MCP Fundamentals Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Objective The objective of this assignment is to help you: Understand advanced agentic MCP capabilities (Sampling, Elicitation, Roots) Design multi-agent systems with appropriate orchestration patterns Analyze security implications of agentic workflows Implement human-in-the-loop design patterns Reason about long-running workflows and error handling Think critically about prod

Course: MCP Fundamentals Level: Medium Type: Individual Assignment Duration: 4–5 days Objective The objective of this assignment is to help you: Understand the architectural problem MCP solves and why earlier approaches failed Master the distinction between Tools, Resources, and Prompts Apply primitive selection logic to real-world integration scenarios Design MCP Server architectures with appropriate primitives Analyze trade-offs in transport mechanisms and deployment m

Objective In this assignment, you will move beyond isolated chunking techniques and design a complete, adaptive chunking system that intelligently selects or combines strategies based on the input document type. This is closer to how chunking is actually used in production systems. Problem Statement Most tutorials treat chunking strategies independently: Fixed-size chunking Overlapping chunking Sentence-based chunking Token-aware chunking Semantic chunking However, in real-w

Course: Chunking Strategies for Production RAG Systems Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Objective The objective of this assignment is to help you: Understand and implement multiple chunking strategies Analyze trade-offs between different approaches Design a hybrid chunking pipeline Evaluate chunking quality in a Retrieval-Augmented Generation (RAG) context Think like an engineer building production-ready systems Problem Statement You a

Course: RAG Evaluation Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to evaluate the generation stage of a RAG pipeline, attribute failures to the correct pipeline stage, and automate the entire evaluation workflow using an LLM as a judge. You will generate RAG answers, measure faithfulness and completeness, run end-to-end error attribution, build a structured LLM judge, and compare automated scores against your

Course: Docker for AI Apps Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to design and operate a multi-container Docker system for an AI application. You will configure container-to-container networking using a user-defined bridge network, orchestrate a multi-service stack with Docker Compose, build and containerize a FastAPI AI REST API with session management and health checks, apply Docker best practices incl

Introduction Modern satellite constellations generate petabytes of multispectral, hyperspectral, SAR, and LiDAR data every day, far outpacing the capacity of traditional analysis methods. Remote sensing professionals must interpret this imagery against historical baselines, evolving scientific literature, environmental benchmarks, and mission-specific requirements simultaneously. Satellite Data Analysis Systems powered by Retrieval-Augmented Generation (RAG) address this by d

Introduction Loan underwriting requires the rapid processing of vast financial documents, regulatory guidelines, and market data under tight deadlines, a challenge that rigid scoring models and manual review workflows are ill-equipped to handle. Underwriters must assess creditworthiness, collateral quality, and compliance requirements while keeping pace with constantly shifting lending regulations and economic conditions. Loan Underwriting Systems powered by Retrieval-Augment

Introduction Traditional AI interactions require rigid command structures limiting natural communication. Developers struggle to extract optimal responses from language models without specialized knowledge. Manual experimentation with different prompting approaches consumes significant development time. Inconsistent model outputs complicate production deployment and user experience. Llama 3:8B Chat transforms AI interactions through instruction-tuned conversational capabiliti

Introduction The exponential growth of scientific literature, with millions of papers published annually, has made it increasingly difficult for researchers to keep pace with complex technical content. Traditional approaches based on manual reading and note taking create bottlenecks in knowledge discovery as scientists spend countless hours deciphering dense methodologies and synthesizing findings. Scientific Text Comprehension powered by Retrieval Augmented Generation (RAG)

Introduction Project management systems handle vast numbers of tasks daily across organizations, creating complex workflows that demand...

Introduction Email marketing campaigns generate vast volumes of messages across automation platforms, newsletters, and promotional...

Introduction Social media platforms generate massive volumes of user generated content containing valuable insights about brand...

Introduction The e-commerce market handles vast volumes of transactions, with retailers managing extensive product catalogs, dynamic...

Introduction Modern applications generate and store enormous amounts of data across diverse database systems, including relational,...

Introduction Healthcare professionals face increasingly complex challenges in clinical decision making, as medical knowledge is expanding at an unprecedented pace and new clinical studies are published daily. Physicians must process vast amounts of literature, adapt to constantly evolving guidelines, and interpret complex patient data to make accurate diagnoses and treatment plans. Traditional support systems, based on static rules and limited knowledge bases, often fail to r