Claude AI Training in DIFC for Corporates & Business Leaders

MODULE 1Foundations and Prompting

• What a large language model does, and what it cannot do
• The Claude ecosystem: Chat, Projects, Artifacts, Skills, Connectors, Cowork
• The TAGS_C framework: Task, Audience, Goal, Style, Context
• Working with PDFs, policies, spreadsheets and long email threads
• Accuracy habits: verification, source checks, catching confident errors

MODULE 2Building Your Second Brain with Claude Projects

• Why one good Project beats a hundred one-off chats
• Loading SOPs, formats, templates, client and product history
• Custom instructions and tone control for consistent output
• Skills: packaging a repeated task into a reusable asset
• Sharing Projects so a department works from one brain

MODULE 3Making Quick Productivity Apps with Claude Artifacts

• What Artifacts are: documents, tables, dashboards and small working apps
• Building calculators, trackers, checklists and scorecards in plain English
• Turning raw data into charts and one page summaries
• Changing, extending and sharing an app by conversation
• Where an Artifact replaces a fragile sheet or a queued IT request

MODULE 4Building AI Coworkers with Claude Cowork

• The shift from asking questions to handing over the job
• Giving Claude access to folders and files, and what stays out of bounds
• Connectors: mail, calendar, drive and business systems in one flow
• Writing a brief an AI coworker can execute, with checkpoints
• Examples: report packs, data clean-up, deck building, mailbox triage

MODULE 5Always-On Systems and Governance

• Scheduled routines: daily briefings, weekly reports, follow-up trackers
• Designing a trigger, action and output routine for your own work
• Function playbooks across sales, marketing, HR, finance and operations
• Guardrails: confidentiality, data handling, approvals and audit trails
• Building a personal thirty day automation plan

MODULE 1How Generative AI Really Works

• The prediction machine — tokens, the next-token loop, why outputs are probabilistic
• Inside the model — embeddings and attention without the math, visualised
• Context windows — what fits, what gets lost, why size matters
• The three ages of AI — prompt engineering, RAG, agentic
• Strengths and failure modes — hallucination, stale knowledge, brittle math

MODULE 2The Claude Ecosystem — Models & Products Deep Dive

• The four superpowers — long context, instruction following, agentic design, enterprise safety
• Claude AI deep dive — Artifacts, Projects, Files, Memory, Styles, Search
• Claude Code and Claude Cowork — who each is for and where they fit
• Skills, Connectors and Plugins at a glance
• The decision tree — given a task, which product and which model

MODULE 3Design Discipline — Plan First, Build Second

• The four eras — traditional, prompt engineering, spec-driven, spec IS the project
• Why vibe coding fails — AI invents architecture you never approved
• Architecture-first thinking — problem statement, domain model, folder layout
• Plan mode vs act mode — what each does and when to switch
• When to break the rule — spikes, prototypes and throwaway code

MODULE 4Claude Code and CLAUDE.md — Your Project’s Brain

• Setup and first commands — install, auth, project memory, permissions, slash commands
• The build rhythm — prompt, plan, review, act, test, repeat
• Anatomy of a great CLAUDE.md — context, stack, conventions, commands, gotchas
• Writing for Claude, not for humans — what to spell out, where examples go
• The four anti-patterns — dumping ground, stale spec, wall of rules, vague vibe

MODULE 5Skills, RAG & Vector Databases

• Progressive disclosure — metadata scan, full load, supporting files
• SKILL.md anatomy and the description field as the trigger engine
• The RAG mental model — retrieve, augment, generate
• Chunking strategies, embeddings, and choosing a vector store
• RAG vs alternatives — when long context wins, when SQL wins, when fine-tuning wins

MODULE 6Agents, Sub-Agents & Token Economics

• Workflow vs agent — the crisp definition, and why most “agents” are workflows
• Core patterns — tool-use loop, ReAct, planner-executor, orchestrator-worker
• Tool use anatomy — JSON schemas, parallel calls, error handling and retries
• Sub-agents — own context, own prompt, own tools, own model
• Token optimization — prompt caching, model routing, batch API, Skills as token-savers