Course Outline
Phase 1 — Meet Claude Code — 55 minutes
- What Claude is, and what distinguishes Claude Code from standard chat interfaces.
- The Claude product family: claude.ai, Claude Desktop, Claude Code (CLI), and their relationships.
- Interface tour: navigating the Claude app, initiating a coding session, and understanding the workspace.
- How Claude Code thinks: the describe → plan → act → review loop.
- Understanding permissions: why Claude requests approval before creating files or running code.
- Your first build: asking Claude to create a simple styled webpage from a one-sentence description.
- Iterating on results: prompts like “make the header bigger,” “change the color scheme,” or “add a navigation bar.”
- Guided exercise: Participants open the Claude app, start a Claude Code session, and build a personalized “About Me” webpage by describing their desires in plain English. They practice refining results through follow-up instructions.
Goal: everyone is comfortable with the interface and has overcome the first-interaction hurdle.
Break — 10 minutes
Phase 2 — Building Real Things with Plain English — 70 minutes
This is the core of the morning. Participants complete four progressively complex tasks using only natural language prompts.
- Task 1 — Interactive dashboard: Ask 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,” or “make it responsive.”
- Task 2 — Data analysis: Provide Claude with a sample CSV file and ask it to summarize the data, identify trends, find highest and lowest values, and generate a visual chart. 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 shows how Claude transforms raw data into polished deliverables.
- Task 4 — Automation tool: Ask Claude to build a simple utility — such as 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 what happened behind the scenes: which files were created, what code was written, and how to interpret the output. Participants document their best prompts in a shared Prompt Playbook.
Break — 10 minutes
Phase 3 — Working Smarter with Claude Code — 50 minutes
- The art of good prompting: specific vs. 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 try a different approach.
- Working with uploaded files: prompts like “read this document and summarize it,” or “convert this spreadsheet into a chart.”
- Multi-step workflows: chaining requests to build complex outputs (e.g., “first analyze this data, then build a dashboard from the results.”)
- Understanding cost and usage: how tokens, context windows, and subscription tiers work.
- When to use Claude Code vs. regular Claude chat.
- Guided exercise: Participants take 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: level up from “it works” to “I can get great results consistently.”
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 technical aspects, but participants call the shots by naming real problems from their jobs, suggesting prompt ideas, and debating tradeoffs. The goal is to learn prompt judgment by watching someone skilled 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’d normally write from scratch (proposals, emails, job descriptions, social posts)
- Analyze — interrogate a document or dataset you don’t 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 how the session works. Participants submit real workflow problems from their jobs via a shared doc or chat.
Live build #1 — Transform workflow (20 min): The instructor picks one submitted problem, builds it live with the room calling out prompt ideas, pushbacks, and refinements. The instructor narrates every choice. The session ends with a working prompt template that the participant whose problem it was gets to keep.
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 write down one prompting move that surprised them, one thing they’d do differently, and one pattern they’re taking home. Quick group share — 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: turning 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 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 (instructor provides a test URL).
- Option C: Install a Desktop Extension from the marketplace (for Claude Desktop users).
Participants then give Claude a task that uses 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 see Claude as a connective layer between all the services they already use, not just a coding tool.
Break — 10 minutes
Phase 6 — Capstone & Next Steps — 65 minutes
Capstone mini-project (45 min): Each participant chooses 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 analyze 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
An understanding of
- Basic computer literacy: navigating files and folders, using a web browser, and installing applications
- General awareness of AI assistants’ functions (e.g., casual use of ChatGPT, Gemini, or Claude provides helpful context, but it is not mandatory)
Experience with
- No coding, programming, or terminal experience is required. This course is designed for individuals who have never written a line of code.
- No prior experience with Claude or any other AI tool is necessary.
Technical requirements
- Participants should bring a laptop (Mac, Windows, or Linux) equipped with a modern web browser.
- A stable internet connection is required.
- A Claude Pro subscription for the session (a 1-month gift subscription is included with course registration; setup instructions are sent prior to class).
- Claude Desktop is recommended but not mandatory (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), although alternative connector options are available.
Audience
- Business professionals looking to leverage AI for productivity and automation.
- Marketers, operations managers, and analysts seeking to automate repetitive tasks.
- Founders and entrepreneurs who want to build prototypes without hiring a developer.
- Educators and researchers exploring AI-assisted workflows.
- Anyone curious about what Claude can build who lacks a technical background.
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
