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Purpose In Assignment 1, you built the individual building blocks of a multi-agent system. In this assignment, you move beyond isolated concepts and design a complete, production-oriented orchestration engine  that can dynamically configure agent pipelines, select routing strategies based on system state, handle errors gracefully, and adapt to different workflow requirements at runtime. This assignment is closer to how multi-agent systems are actually designed in production e

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

ASSIGNMENT REQUIREMENT DOCUMENT Course Name:  Hybrid Search and Re-ranking — From Retrieval to Reliable Answers Institution:  [Institution Name] Semester:  [Semester / Term — e.g., Spring 2026] Instructor:  [Instructor Name] Student Level:  Postgraduate / Senior Undergraduate (Year 3–4) Submission Platform:  Moodle LMS Total Assignments:  2 Note to Students:  This assignment contains the complete requirements for one of the  course assignments . Read the entire document caref

Course:  LLM Observability — From Traces to Incident Response Chapters Covered:  7 – 10 (Trace-Based Debugging & Replay, Incident Response, LangSmith vs Langfuse in Real Teams, Final Lab & Packaging) Level:  Medium → Advanced Type:  Individual Assignment Duration:  7 – 10 days Prerequisite:   familiarity with trace schemas, metrics, and tracing concepts from Chapters 1–6 Objective By the end of this assignment you will be able to: Load, inspect, and diagnose production trace

Course:  LLM Observability — From Traces to Incident Response Chapters Covered:   1 – 6 (Why LLM Observability, Environment Setup, LangSmith Setup, Langfuse Setup, Instrumentation Design, Metrics & Dashboards) Level:  Medium → Advanced Type:  Individual Assignment Duration:  7 – 10 days Objective By the end of this assignment you will be able to: Articulate why traditional monitoring fails  for LLM applications and identify the observability gap. Design a Pydantic-based trace

Course:  Metadata Filtering Level:  Medium → Advanced Type:  Individual Assignment Duration:  7–10 days Objective The objective of this assignment is to help you: Build dynamic filter construction  driven by user context (role, department, organization) Implement query-time filter inference  — extracting metadata filters from natural language queries Design a hybrid search  system that combines keyword matching, vector similarity, and metadata filtering Enforce non-overridabl

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:  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-Augmented Ge

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: 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