Course Outline
Phase 1 — Meet Claude Code — 55 minutes
- An overview of what Claude is and what sets Claude Code apart from standard chat
- The Claude product family: claude.ai, Claude Desktop, Claude Code (CLI), and their interrelations
- Interface tour: navigating the Claude app, initiating a coding session, and understanding the workspace
- How Claude Code approaches tasks: the describe → plan → act → review loop
- Understanding permissions: why Claude seeks approval before creating files or executing code
- Your first build: instructing Claude to create a simple styled webpage from a one-sentence description
- Iterating on results: commands such as “make the header bigger,” “change the colour scheme,” “add a navigation bar”
- Guided exercise: participants open the Claude app, start a Claude Code session, and build a personalised “About Me” webpage by describing their requirements in plain English. They practice refining results through follow-up instructions.
Goal: everyone feels comfortable with the interface and overcomes the first-interaction hurdle.
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Break — 10 minutes |
Phase 2 — Building Real Things with Plain English — 70 minutes
This forms the core of the morning session. Participants complete four progressively complex tasks using only natural language prompts.
- Task 1 — Interactive dashboard: instruct Claude Code to build a styled dashboard displaying sample data with charts, statistics, and a clean layout. Practice providing design direction: “use a dark theme,” “add a sidebar,” “make it responsive.”
- Task 2 — Data analysis: provide Claude with a sample CSV file and request a data summary, trend identification, highest and lowest value detection, and a visual chart generation. This demonstrates how Claude writes and executes code on your behalf.
- Task 3 — Document generator: ask Claude to read a data file and produce a formatted report — such as a sales summary, project status update, or meeting recap. This showcases how Claude transforms raw data into polished deliverables.
- Task 4 — Automation tool: ask Claude to build a simple utility — a unit converter, quiz app, or budget calculator. This introduces the concept that Claude can create interactive tools, not just static pages.
After each task, the instructor highlights Claude’s behind-the-scenes actions: which files were created, what code was written, and how to interpret the output. Participants document their most effective prompts in a shared Prompt Playbook.
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Break — 10 minutes |
Phase 3 — Working Smarter with Claude Code — 50 minutes
- The art of effective prompting: specific versus vague instructions
- Live demo: side-by-side comparison of weak and strong prompts on the same task
- Iterating and refining: asking Claude to explain its choices, undo changes, or attempt a different approach
- Working with uploaded files: “read this document and summarise it,” “convert this spreadsheet into a chart”
- Multi-step workflows: chaining requests to construct complex outputs (“first analyse this data, then build a dashboard from the results”)
- Understanding cost and usage: how tokens, context windows, and subscription tiers operate
- When to use Claude Code versus standard Claude chat
- Guided exercise: participants select one of their Phase 2 projects and extend it with two new features using a multi-step prompt chain. They then compare their before-and-after prompts to identify what made the difference.
Goal: progress from “it works” to “I can achieve great results consistently.”
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Break — 10 minutes |
Phase 4 — Your Claude Workflows: Live Build Session — 60 minutes
This phase shifts the energy in the room. Instead of solo practice, the group builds together. The instructor drives the process, but participants call the shots — introducing real problems from their own jobs, suggesting prompt ideas, and debating tradeoffs. The objective is to learn prompt judgment by observing a skilled practitioner navigate uncertainty in real time.
Three workflow archetypes structure the session:
- Transform — take input X, produce output Y (meeting notes → action items; raw data → summary email; customer feedback → themed report)
- Draft — generate a first version of something you would normally write from scratch (proposals, emails, job descriptions, social posts)
- Analyse — interrogate a document or dataset you do not have time to read carefully (a 40-page report, a spreadsheet of survey responses, a contract)
Setup and framing (10 min): The instructor introduces the three archetypes and explains the session mechanics. Participants submit real workflow challenges from their jobs via a shared document or chat.
Live build #1 — Transform workflow (20 min): The instructor selects one submitted problem, builds it live while the room calls out prompt ideas, pushbacks, and refinements. The instructor narrates every decision. The session concludes with a working prompt template that the participant who submitted the problem retains.
Live build #2 — Draft or Analyze workflow (20 min): Same format, different archetype, different participant’s problem.
Reflection & share-back (10 min): Participants take a moment to note one prompting move that surprised them, one thing they would do differently, and one pattern they are taking home. A quick group share occurs — 3-4 voices, not everyone. The instructor connects observations to the broader Prompt Playbook.
Phase 5 — Connecting Claude to Your Tools with MCP — 50 minutes
- What is MCP (Model Context Protocol)? The universal plug system for AI tools
- Why MCP matters: transforming Claude from a chat assistant into a connected workflow hub
- The Connectors Directory: browsing and adding integrations directly from the Claude app
- Desktop Extensions: one-click installs for Claude Desktop (no configuration files needed)
Live demo: The instructor connects Claude to two services through the Connectors UI and demonstrates cross-tool workflows:
- “Check my Google Calendar for tomorrow’s meetings and draft a prep email for each one”
- “Read the latest updates from our project board and write a status summary”
- “Pull data from this connected service and build a local report from it”
Guided exercise: participants connect Claude to at least one service. Options are provided for different comfort levels:
- Option A: Connect a pre-built connector from the directory (e.g., Gmail, Google Drive, or a demo service) — click, authenticate, and go
- Option B: Add a custom connector by pasting an MCP server URL (the instructor provides a test URL)
- Option C: Install a Desktop Extension from the marketplace (for Claude Desktop users)
Participants then assign Claude a task that utilises the connected service — e.g., “Read my recent emails about project updates and create a summary document.”
Key concepts covered:
- How connectors work: OAuth authentication, permissions, and what access you are granting
- Managing tool access: enabling, disabling, and controlling which connectors Claude can use per conversation
- Security awareness: connecting only to trusted services and reviewing tool permissions
- The MCP ecosystem: where to find new connectors, extensions, and community-built servers
Goal: participants view Claude as a connective layer between all the services they already use, rather than just a coding tool.
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Break — 10 minutes |
Phase 6 — Capstone & Next Steps — 65 minutes
Capstone mini-project (45 min): Each participant selects one scenario and builds it with Claude:
- A polished landing page or portfolio site for their team, project, or personal brand
- A data analysis pipeline: upload a file, have Claude analyse it, and produce a visual report
- An interactive tool that solves a real problem from their workflow (calculator, tracker, converter, quiz)
- A connected workflow: pull data from a connected service, transform it, and produce a deliverable (e.g., “read my calendar for next week and build a visual schedule”)
The instructor circulates, helps refine prompts, and showcases standout examples to the group.
Showcase and wrap-up (20 min):
- 6-8 participants share what they built (2-3 min each)
- Where to go from here: Claude Code CLI for terminal users, VS Code extension for developers, Cowork for knowledge workers
- The MCP ecosystem: finding and evaluating new connectors, extensions, and community servers
- Plans: Free vs. Pro vs. Max — what each unlocks and which fits which use case
- Best practices recap: the Prompt Playbook patterns that worked best during the session
- Recommended resources: official documentation, community channels, Anthropic’s prompt engineering guide
- Participants receive a reference card with key prompting patterns, connector setup steps, and a curated list of useful MCP integrations
Requirements
Requirements
Understanding of
- Basic computer literacy: navigating files and folders, using a web browser, and installing applications
- General awareness of AI assistant capabilities (e.g., casual experience with ChatGPT, Gemini, or Claude is helpful context but not mandatory)
Experience with
- No coding, programming, or terminal experience is required. This course is tailored for individuals who have never written code.
- No prior experience with Claude or any other AI tools is necessary.
Technical Requirements
- Participants must bring a laptop (Mac, Windows, or Linux) with a modern web browser
- A stable internet connection
- A Claude Pro subscription for the session (a 1-month gift subscription is included with registration; setup instructions are sent beforehand)
- Claude Desktop is recommended but not essential (the web app at claude.ai is sufficient for all exercises)
- A Google account is recommended for the MCP connectors exercise (Gmail, Google Drive, Google Calendar), though alternative connector options are available
Target Audience
- Business professionals seeking to leverage AI for productivity and automation
- Marketers, operations managers, and analysts aiming to automate repetitive tasks
- Founders and entrepreneurs wanting to build prototypes without hiring a developer
- Educators and researchers exploring AI-assisted workflows
- Anyone curious about Claude’s capabilities who lacks a technical background
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
