<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Codersarts]]></title><description><![CDATA[Programming Assignment Help Service,  Development service, instant one-on-one help for software developers, code reviewing, debugging, and online.]]></description><link>https://www.codersarts.com/blog</link><generator>RSS for Node</generator><lastBuildDate>Sun, 19 Apr 2026 22:08:35 GMT</lastBuildDate><atom:link href="https://www.codersarts.com/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Build Your Portfolio Data Projects Efficiently]]></title><description><![CDATA[Building a strong portfolio is essential when you want to showcase your skills in data science. It helps you stand out to employers, clients, or collaborators. But creating a portfolio can feel overwhelming if you don’t know where to start or how to organize your work. I’m here to guide you through the process step-by-step. By the end, you will have a clear plan to build your portfolio data projects efficiently and effectively. Why You Need a Portfolio Data Projects A portfolio is more than...]]></description><link>https://www.codersarts.com/post/build-your-portfolio-data-projects-efficiently</link><guid isPermaLink="false">69dda9c3602a28f5308b6cdc</guid><category><![CDATA[Data science sample work]]></category><category><![CDATA[Agentic AI]]></category><category><![CDATA[AI Services]]></category><pubDate>Fri, 17 Apr 2026 16:56:12 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/90b6f2_32063651debb42e6bb1e6d87bc827ac0~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Codersarts</dc:creator></item><item><title><![CDATA[Explore Codersarts: Your Expert Coding Services Partner]]></title><description><![CDATA[When you need expert coding services, finding the right partner can make all the difference. Whether you are working on a complex project, debugging code, or building a new application, having reliable support is crucial. I want to introduce you to a platform that can help you move faster and smarter in your coding journey. This platform offers on-demand expert help, mentorship, and development services tailored to your needs. Let me walk you through how this service works and why it could be...]]></description><link>https://www.codersarts.com/post/explore-codersarts-your-expert-coding-services-partner</link><guid isPermaLink="false">69d470bec53e2b8fe1258318</guid><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Coding Assignments]]></category><pubDate>Wed, 08 Apr 2026 05:00:48 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/90b6f2_0d13dc5eb720450899c8b87d9f1747d0~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Codersarts</dc:creator></item><item><title><![CDATA[Research Assistant with AI Sampling]]></title><description><![CDATA[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 patterns, and...]]></description><link>https://www.codersarts.com/post/research-assistant-with-ai-sampling</link><guid isPermaLink="false">69cf4860535e7bcd2699a099</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Coursework Help]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><pubDate>Fri, 03 Apr 2026 05:11:30 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Building an Intelligent Task Management Server]]></title><description><![CDATA[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 and implement...]]></description><link>https://www.codersarts.com/post/building-an-intelligent-task-management-server</link><guid isPermaLink="false">69cf44bef7044e6cf7aa42a2</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><category><![CDATA[Mentorship]]></category><pubDate>Fri, 03 Apr 2026 04:55:07 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Building a FastA2A Orchestrator with Streaming, Multi-Turn Context, and Framework Integration]]></title><description><![CDATA[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 Course Learning...]]></description><link>https://www.codersarts.com/post/building-a-fasta2a-orchestrator-with-streaming-multi-turn-context-and-framework-integration</link><guid isPermaLink="false">69cf40282a4608ae001e6581</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><category><![CDATA[Mentorship]]></category><pubDate>Fri, 03 Apr 2026 04:38:01 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[AI-Powered Emergency Response Agent: Real-Time Disaster Decision Support]]></title><description><![CDATA[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, historical incident...]]></description><link>https://www.codersarts.com/post/ai-powered-emergency-response-agent-real-time-disaster-decision-support</link><guid isPermaLink="false">69ce4d472a4608ae001c6e1a</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Computer Vision Projects]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><category><![CDATA[Deep Learning Projects]]></category><pubDate>Thu, 02 Apr 2026 12:24:33 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_bd686342d66141f89030208a61fd4a2b~mv2.png/v1/fit/w_1000,h_630,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Designing and Implementing a FastA2A Agent Server with Tasks, Messages, and Discovery]]></title><description><![CDATA[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 lifecycle....]]></description><link>https://www.codersarts.com/post/designing-and-implementing-a-fasta2a-agent-server-with-tasks-messages-and-discovery</link><guid isPermaLink="false">69ce546f535e7bcd2697ab06</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><pubDate>Thu, 02 Apr 2026 12:01:35 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Robot Programming Assistance using RAG: Accelerating Industrial Automation with AI Knowledge Systems]]></title><description><![CDATA[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 standards, code...]]></description><link>https://www.codersarts.com/post/robot-programming-assistance-using-rag-accelerating-industrial-automation-with-ai-knowledge-systems</link><guid isPermaLink="false">69ce314340e74dbec4fd9030</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Agentic AI]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><category><![CDATA[Computer Vision Projects]]></category><category><![CDATA[Deep Learning Projects]]></category><pubDate>Thu, 02 Apr 2026 11:59:15 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_ac39d59f39db419588ca82b45245a60d~mv2.png/v1/fit/w_1000,h_630,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Domain-Specific LLM Cost Optimization Strategy &#38; Implementation]]></title><description><![CDATA[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. Design novel...]]></description><link>https://www.codersarts.com/post/domain-specific-llm-cost-optimization-strategy-implementation</link><guid isPermaLink="false">69cdf9ca40e74dbec4fd1617</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><pubDate>Thu, 02 Apr 2026 05:14:40 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Designing and Implementing a Complete LLM Cost Optimization Pipeline]]></title><description><![CDATA[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, compress...]]></description><link>https://www.codersarts.com/post/designing-and-implementing-a-complete-llm-cost-optimization-pipeline</link><guid isPermaLink="false">69cdf5b640e74dbec4fd0e6f</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><category><![CDATA[Coding Assignments]]></category><pubDate>Thu, 02 Apr 2026 05:07:27 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Client Agent Discovery, Registries, and Agent Card Security]]></title><description><![CDATA[Course: Agent Discovery &#38; Agent Cards Level: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the Client Agent side of agent discovery, design and operate a shared Agent Registry, and harden an agent discovery system against trust and security failures. By completing this assignment, you will have implemented the complete five-step Client Agent discovery workflow, built both exact and LLM-assisted skill matching, run a...]]></description><link>https://www.codersarts.com/post/assignment-2-client-agent-discovery-registries-and-agent-card-security</link><guid isPermaLink="false">69cdedf2f7044e6cf7a769f4</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><pubDate>Thu, 02 Apr 2026 04:28:35 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Writing and Publishing Agent Cards]]></title><description><![CDATA[Course: Agent Discovery &#38; Agent Cards Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your understanding of why agent discovery exists, what every field in an Agent Card communicates, and how to publish a valid, discoverable Agent Card from an A2A server. By completing this assignment, you will have written Agent Cards from scratch, identified and fixed common authoring mistakes, served cards via both FastAPI and FastA2A, and built a dynamic...]]></description><link>https://www.codersarts.com/post/writing-and-publishing-agent-cards</link><guid isPermaLink="false">69cdeb5f2a4608ae001b9288</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><pubDate>Thu, 02 Apr 2026 04:17:08 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Efficiently Scale Your AI Applications: ai application scaling tips]]></title><description><![CDATA[Scaling AI applications can feel overwhelming. But it does not have to be. I will guide you through clear, practical steps to help you grow your AI projects efficiently. Whether you are building a prototype or managing a live system, these tips will help you handle increased demand without losing performance or control. Understand Your AI Application Scaling Tips Before you start scaling, you need to understand what scaling means for your AI application. Scaling is about handling more users,...]]></description><link>https://www.codersarts.com/post/efficiently-scale-your-ai-applications-ai-application-scaling-tips</link><guid isPermaLink="false">69c189dd69ed8cb882ab2a73</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Agentic AI]]></category><pubDate>Wed, 01 Apr 2026 20:53:18 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/90b6f2_53e423524ab04b329956ee0ce506c781~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Codersarts</dc:creator></item><item><title><![CDATA[Get Fast Machine Learning Support: Your Guide to Immediate Help]]></title><description><![CDATA[When you are working on a machine learning project, time is often critical. Whether you are a student struggling with an assignment, a developer debugging code, or a startup founder trying to launch an AI product, you need fast, reliable support. I understand how frustrating it can be to hit a roadblock and not know where to turn. That is why I want to guide you through the best ways to get fast machine learning support  and keep your project moving forward. How to Find Fast Machine Learning...]]></description><link>https://www.codersarts.com/post/get-fast-machine-learning-support-your-guide-to-immediate-help</link><guid isPermaLink="false">69cb34e320141e70489e3f56</guid><pubDate>Wed, 01 Apr 2026 20:50:28 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/90b6f2_57dd77e27da442fe9a51502ab3e9896b~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Codersarts</dc:creator></item><item><title><![CDATA[Building an Adaptive Multi-Agent Orchestration Engine with Dynamic Routing and Workflow Management]]></title><description><![CDATA[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 environments....]]></description><link>https://www.codersarts.com/post/building-an-adaptive-multi-agent-orchestration-engine-with-dynamic-routing-and-workflow-management</link><guid isPermaLink="false">69c4c2add2e55f64fe09561a</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><pubDate>Thu, 26 Mar 2026 05:49:56 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Designing and Implementing a Multi-Agent Collaboration Framework]]></title><description><![CDATA[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 clarity of your...]]></description><link>https://www.codersarts.com/post/designing-and-implementing-a-multi-agent-collaboration-framework</link><guid isPermaLink="false">69c3cb6cd2e55f64fe07461f</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><pubDate>Thu, 26 Mar 2026 05:14:20 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Designing an Adaptive Chunking Engine for Real-World RAG Systems]]></title><description><![CDATA[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 structural failure modes...]]></description><link>https://www.codersarts.com/post/designing-an-adaptive-chunking-engine-for-real-world-rag-systems-1</link><guid isPermaLink="false">69c3bb87dbf1d5b6013102d1</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><category><![CDATA[Data Science]]></category><category><![CDATA[Python]]></category><pubDate>Wed, 25 Mar 2026 11:38:08 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Designing a Production-Ready Chunking Pipeline for Retrieval-Augmented Generation]]></title><description><![CDATA[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 carefully before...]]></description><link>https://www.codersarts.com/post/designing-a-production-ready-chunking-pipeline-for-retrieval-augmented-generation</link><guid isPermaLink="false">69c3b5edd2e55f64fe0719aa</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><pubDate>Wed, 25 Mar 2026 10:32:49 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Debugging, Incident Response, and Postmortem for LLM Systems]]></title><description><![CDATA[Course:  LLM Observability — From Traces to Incident Response Chapters Covered:  7 – 10 (Trace-Based Debugging &#38; Replay, Incident Response, LangSmith vs Langfuse in Real Teams, Final Lab &#38; 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 failures  across...]]></description><link>https://www.codersarts.com/post/debugging-incident-response-and-postmortem-for-llm-systems</link><guid isPermaLink="false">69c3a650a0626dd190094f10</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><pubDate>Wed, 25 Mar 2026 09:30:39 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item><item><title><![CDATA[Instrumenting and Monitoring an LLM Application for Production]]></title><description><![CDATA[Course:  LLM Observability — From Traces to Incident Response Chapters Covered:   1 – 6 (Why LLM Observability, Environment Setup, LangSmith Setup, Langfuse Setup, Instrumentation Design, Metrics &#38; 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 schema  that...]]></description><link>https://www.codersarts.com/post/instrumenting-and-monitoring-an-llm-application-for-production</link><guid isPermaLink="false">69c3a2c9149f4fed56515618</guid><category><![CDATA[AI Services]]></category><category><![CDATA[Coding Assignments]]></category><category><![CDATA[Coding Exercises]]></category><category><![CDATA[Project Support]]></category><category><![CDATA[Mentorship]]></category><category><![CDATA[Large Language Models (LLMs)]]></category><category><![CDATA[Machine Learning Projects]]></category><category><![CDATA[NLP Projects]]></category><pubDate>Wed, 25 Mar 2026 09:08:28 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/232056_1d1615f5a8e942cfabded225a4c00bbc~mv2.png/v1/fit/w_1000,h_773,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>ganesh90</dc:creator></item></channel></rss>