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Assignment Overview Scenario: You are a research engineer at an academic institution building tools to help researchers manage and analyze scientific literature. Your task is to create an advanced MCP server that not only provides access to research papers but also uses AI sampling (server-initiated LLM calls) to generate intelligent summaries, extract key findings, and compare papers. This assignment builds on Assignment 1 by adding Module 4 concepts: sampling, production pa

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: 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

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

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 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

Introduction In the fast-paced world of software engineering, development teams often waste time searching through scattered...

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...

Ever wished you had a friend who's traveled everywhere, knows all the hidden gems, and could instantly create the perfect itinerary for...

Picture this: You're at your computer, juggling three different databases for three different projects. You keep switching connection...

Prerequisite:  This is a continuation of the blog Part 4: A Complete Guide to Creating a Multi-Agent Book Writing System Solutions for...