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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: RAG Evaluation Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the two foundational components of any RAG evaluation workflow: a golden dataset and a retrieval quality report. Without a golden dataset, no evaluation metric has meaning. Without retrieval evaluation, you cannot tell whether failures come from the retrieval stage or the generation stage. By completing this assignment, you will have a

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

Course: Docker for AI Apps Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to work with Docker's core building blocks: running and inspecting containers, writing a production-ready Dockerfile, containerizing a Python AI application, and persisting data across container restarts using named volumes. By completing this assignment, you will have built and deployed a fully containerized multi-turn AI chatbot that retai

Course: RAG from Scratch Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the retrieval and generation stages of a RAG pipeline from scratch. You will implement cosine similarity without external vector search libraries, build a similarity search function, design a grounding-focused prompt template, and assemble a complete end-to-end RAG system that retrieves context and generates accurate, grounded answer

Course: RAG from Scratch Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your ability to build the foundational stages of a RAG pipeline: loading documents, extracting clean text, attaching metadata, enriching documents with LLM-generated keywords, and splitting them into retrievable chunks. By completing this assignment, you will have built a reusable knowledge base preparation pipeline that you can apply to any document colle

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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

Prerequisite: This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 3 The Connection Traffic Controller - list_database_connections @mcp.tool() async def list_database_connections() -> str: """List all active database connections""" connections = {} for key, manager in connection_pool.items(): db_type, db_path = key.split(':', 1) connections[key] = { 'database_path': db_path, 'database_type': db_type, 'is_sample_dat

Prerequisite: This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 2 Meet...

Prerequisite: This is a continuation of the blog Part 2: Building a Database MCP Server: The Ultimate Code Walkthrough - Part 1 The...

Prerequisite: This is a continuation of the blog Part 2: A Complete Guide to Creating a Multi-Agent Book Writing System 🧰 Tool Definitions Let’s now define all our MCP tools, one by one. 📅 Get Full Itinerary @mcp.tool() async def get_full_itinerary(city: str, days: int = 5) -> str: """Get a complete travel itinerary with direct Ollama AI using file-based data. Args: city: Name of the city to visit days: Number of days for the itinerary (default: 5) Returns: A formatted m

Prerequisite: This is a continuation of the blog Part 1: A Complete Guide to Creating a Multi-Agent Book Writing System Making Sense of...