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Claude AI

Start Here & FAQs 0/8 ›

Start Here: Claude AI Quick Links: Helpful Resources 2604(April)/2605 (May) Weekend - Course Schedule 2606(June) / 2607(July) Weekend - Course Schedule Claude: 5 Products (Offerings) Claude Cowork vs Claude Code FAQ's: Claude for Agentic AI (SDLC) Program Q/A: Where is Python (Live & Recording) & Bonuses

2605 - Live Session Recordings 0/8 ›

💡Quizzes 📢 Weekly Recap Announcement Post Day 1 & Day 2 Recap: Calude for Agentic AI 260516- Day 1 - Foundations of Claude & Agentic AI 260517 - Day 2 - Enterprise Environment Setup 260523 - Day 3 - Scalable Agentic Architectures 260524 - Day 4 - Reliable Agent Prompts 260531 - Day 5 - AI Code Gen & Standards

Module 1: Foundations of Claude and Agentic AI 0/4 ›

L1: Overview of Claude 3.5 Sonnet & Claude 4 L2: What Is Claude? LLMs Explained for Developers L3: Role of Agents in GenAI Workflows & SDLC L4: Agentic Loop Lifecycle and Correct Termination

Module 2: Setup Environment & Enterprise 0/9 ›

L1: Your First Claude API Call L2: Claude Code — What It Is L3: Setup for UI, Backend & Knowledge Base L4: API/Platform Access, Integrations & Secret Lab 2.1: Create & Setup Claude Account Lab 2.2: Setup VS Code Lab 2.3: Install & Setup Git, Node.js, Claude Code Lab 2.4: Install Chrome Extension for Claude Lab 2.5: Interactive Workspace in Claude Artifact

Module 3 — Designing Scalable Agentic Architecture 0/8 ›

L1: Modular Architecture & The Agent SDK L2: Task Decomposition and Chaining L3: Multi-Agent Orchestration L4: Tool Distribution Across Agents L5: Writing Tool Descriptions L6: MCP Patterns for Real Workflows Lab 1: Task decomposition,Planning,Implementation Lab 2: Build Multi-Agent Pipelines with Claude

Module 4 — Prompt Engineering for Reliable Agent 0/8 ›

L1: Explicit Criteria and Instruction Design L2: Few-Shot Prompting for Consistency L3: Context Optimization L4: Escalation Patterns and Reliable Decision Lab 4.1: Design and test basic prompts for Claude Lab 4.2: Structure prompts for relevant answers. Lab 4.3: Implement context retention systems Lab 4.4: Simulate edge cases & test robustness

Module 5 — AI Powered Code Gen & Engg Standards 0/12 ›

L1: Backend and Frontend Standards L2: API Design (REST/GraphQL) L3: Tool Use for Reliable Structured Output L4: CLAUDE.md Configuration Hierarchy L5: Custom Slash Commands and Skills L6: Plan Mode and Iterative Refinement L7: Structured Error Responses Lab 5.1: Working with Claude Skills Lab 5.2: Generate backend API code (CRUD, Auth). Lab 5.3: Generate frontend components & UI element Lab 5.4: Generate and design RESTful/GraphQL APIs. Lab 5.5: Finetune Claude for context assistance

Module 6 — UI and Experience Generation with AI 0/5 ›

L1: Figma and HTML Integration L2: Design Systems and AI-Driven Scaffoldings L3: Accessibility and Responsiveness Constrain Lab 1: Generate a UI design system in Figma Lab 2: Automatically generate HTML/CSS based

Module 7 — Automated Testing and Quality Engg'ing 0/7 ›

L1: Unit & Integration Test Generation L2: Validation-Retry Loops L3: Multi-Pass Review Strategies L4: Coverage and Quality Metrics Lab 1: Generate unit tests & validate correctness Lab 2: Write integration tests for multiple system Lab 3: Assess test coverage and generate coverage

Module 8 — AI-Driven Documentation & Visualization 0/6 ›

L1: Mermaid and Swimlane Diagrams L2: Technical Documentation Generation L3: BRD to Requirement Traceability Lab 1: Create flowcharts/Mermaid diagrams Lab 2: Automatically generate tech docs Lab 3: Generate a Business Requirements Document

Module 9 — Enterprise Integration & DevOps Align 0/7 ›

L1: Rally Integration & Excel Export L2: GitHub Structure, Commit Standards L3: CI/CD Integration and Session Isolation L4: MCP Server Configuration Lab 1: Integrate Rally with Claude Lab 2: Integrate Claude with GitHub Lab 3: Design a CI/CD pipeline using Claude

Module 10 — Debugging, Optimization & Improvement 0/7 ›

L1: Troubleshooting Common Issues L2: Context Degradation in Extended Sessions L3: Performance Tuning L4: Feedback Loops Lab 1: Intentionally introduce bugs to test Claude Lab 2: Optimize code for memory management, speed Lab 3: Implement feedback loops

Module 11 — Governance, Security & Responsible AI 0/7 ›

L1: Coding Standards, & Ethical AI Practices L2: Hooks and Programmatic Enforcement L3: Human Review, Provenance L4: Version Control and Audit Trails Lab 1: Implement a security/compliance checklist Lab 2: Setup version control tracking for AI model Lab 3: Test outputs for bias and implement filters

Module 12 — Labs & Real-World Agentic Workflows 0/5 ›

L1: IAG Booking (Iberia Pilot) Overview L2: Custom Agent Creation for Niche Workflows L3: Collaborative Development Scenarios Lab 1: Design real-world agentic workflows Lab 2: Hands-on multi-agent workflow

Module 13 — Wrap-Up and Next Steps 0/2 ›

L1: Q&A and Feedback Collection L2: Exam Strategy and Certification Overview

Bonus Labs: Cluade + AWS 0/4 ›

Bonus Lab: Automate AWS Diagrams using Kiro AI Lab: Claude Code on Amazon Bedrock Lab: Claude Design Lab Lab: Claude on AWS Platform

What's Next 0/1 ›

What's Next After Claude AI ?

Home › Claude AI › Module 13 — Wrap-Up and Next Steps

Module 13 — Wrap-Up and Next Steps

2 lessons

1 L1: Q&A and Feedback Collection 2 L2: Exam Strategy and Certification Overview