Codersarts Blog.

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

Assignment Overview Scenario: You are a software engineer at a productivity software company. Your team is developing an AI-powered personal assistant that helps users manage their daily tasks, projects, and goals. Your task is to build an MCP server that provides intelligent task management capabilities to Claude Desktop, allowing users to interact with their task lists using natural language. Learning Objectives: Implement MCP tools for CRUD operations on task data Design a

Purpose In Assignment 1, you built the core protocol and task-handling pieces. In this assignment, you will extend that foundation into a more realistic system that supports streaming responses, multi-turn conversation continuity, framework integration, observability, and production-minded orchestration. This assignment draws heavily on Chapters 6–7 and asks you to show how FastA2A behaves when it is used as the coordination layer for real application workflows. Connection to

Introduction During a disaster, incident commanders must process conflicting data from dozens of sources, coordinate resources across multiple agencies, and make life-safety decisions under extreme time pressure. Traditional tools and static decision trees cannot keep pace with rapidly evolving, multi-agency incidents. AI-Powered Emergency Response Agents built on Retrieval-Augmented Generation (RAG) address this by continuously retrieving real-time situational data, historic

Purpose This assignment requires you to build a working multi-agent service using the FastA2A design principles covered in Chapters 1–5. You will implement agent identity, capability metadata, structured messages, tasks, context handling, routing, and a basic request/response workflow that mirrors the FastA2A protocol model. Your solution should demonstrate how a real agent system moves from discovery to execution and then back to the client with a stable, inspectable task li

Introduction Programming industrial robots requires mastery of multiple proprietary controller languages, motion planning algorithms, safety standards, and constantly evolving vendor documentation. This knowledge burden slows deployments, escalates costs, and creates dangerous skill gaps across automation teams. Robot Programming Assistance Systems powered by Retrieval-Augmented Generation (RAG) address this by dynamically retrieving relevant vendor documentation, safety stan

Duration: 10–14 days following Assignment 1 Type: Individual Assignment Difficulty Level: Advanced Marks: 100 This assignment requires you to create an original, domain-specific cost optimization strategy for a real-world LLM application that you research or design. Unlike Assignment 1 (general framework), Assignment 2 demands independent research, creative problem-solving, and domain expertise. Learning Objectives Research real LLM cost challenges in a specific industry. Des

Course: LLM Cost Engineering — From Token Economics to Production Monitoring Student Level: Undergraduate Year 3 / Postgraduate Submission Platform: Moodle (Learning Management System) Individual / Group: Individual Assignments Purpose This assignment requires you to design and implement a comprehensive cost optimization framework for a real or hypothetical LLM-powered application. You will leverage every core concept introduced across Chapters 1–9: understand token economics

ASSIGNMENT REQUIREMENT DOCUMENT Course:  Agent-to-Agent (A2A) — Multi-Agent Systems in Python Student Level:  Undergraduate Year 3 / Postgraduate Submission Platform:  Moodle (Learning Management System) Individual / Group:  Individual Assignments Total Assignments:  2 This document contains the full specifications for Assignment 1 . Read every section carefully before you begin. You will be assessed on the quality of your implementation, the depth of your analysis, and the c

Purpose In this assignment, you move beyond isolated chunking techniques to design a complete, adaptive chunking system  that intelligently detects document types and selects or combines chunking strategies accordingly. This simulates how chunking is actually deployed in production RAG systems — not as a fixed function, but as a design decision  that adapts to input characteristics. Connection to Course Learning Outcomes (CLOs) CLO Description Relevance CLO 1 Identify structu

Course:  Metadata Filtering Level:  Medium → Advanced Type:  Individual Assignment Duration:  5–7 days Objective The objective of this assignment is to help you: Understand why  metadata filtering is essential for production RAG systems Design a metadata schema  for a real-world knowledge base Implement metadata-preserving chunking  so that chunk-level metadata is never lost Build and apply pre-filters  using ChromaDB's filter syntax Compare pre-filtering vs post-filtering  a

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: 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

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