Course Outline
Phase 1 — Meet Claude Code — 55 minutes
- Understanding what Claude is and how Claude Code differs from standard chat
- 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 approaches problems: the describe → plan → act → review loop
- Understanding permissions: why Claude requests confirmation 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: “make the header larger,” “change the color scheme,” “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 requirements in plain English. They practice refining results through follow-up instructions.
Goal: ensure everyone is comfortable with the interface and has overcome the initial interaction hurdle.
Break — 10 minutes |
Phase 2 — Building Real Things with Plain English — 70 minutes
This is 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 ask it to summarize the data, identify trends, find the 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 — a unit converter, quiz app, or budget calculator. This introduces the concept that Claude can build interactive tools, not just static pages.
After each task, the instructor highlights Claude’s background processes: 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.
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 try a different approach
- Working with uploaded files: “read this document and summarize it,” “convert this spreadsheet into a chart”
- Multi-step workflows: chaining requests to build complex outputs (“first analyze this data, then build a dashboard from the results”)
- Understanding cost and usage: how tokens, context windows, and subscription tiers function
- When to use Claude Code versus 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: elevate skills from “it works” to “I can consistently achieve great results.”
Break — 10 minutes |
Phase 4 — Your Claude Workflows: Live Build Session — 60 minutes
This phase shifts the energy in the room. Instead of solitary practice, the group builds together. The instructor leads, but participants direct the session — naming real problems from their jobs, suggesting prompt ideas, and debating tradeoffs. The objective is to learn prompt judgment by observing an expert 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)
- 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 the session structure. Participants submit real workflow problems from their jobs via a shared document or chat.
Live build #1 — Transform workflow (20 min): The instructor selects one submitted problem and builds it live with the room providing prompt ideas, pushbacks, and refinements. The instructor narrates every choice. The session ends with a working prompt template that the participant who submitted the problem 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 would do differently, and one pattern they are taking home. A quick group share follows — 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 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 give Claude a task that utilizes the connected service — for example, “Read my recent emails about project updates and create a summary document.”
Key concepts covered:
- How connectors work: OAuth authentication, permissions, and the 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, 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
Prerequisite Knowledge
- Basic computer literacy: navigating files and folders, using a web browser, and installing applications
- General awareness of AI assistant capabilities (e.g., casual use of ChatGPT, Gemini, or Claude is helpful context but not mandatory)
Experience Level
- No coding, programming, or terminal experience is required. This course is designed for individuals who have never written code.
- No prior experience with Claude or any other AI tool 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 complimentary subscription is included with registration; setup instructions will be sent prior to the class)
- Claude Desktop is recommended but not required (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
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 wishing to build prototypes without hiring a developer
- Educators and researchers exploring AI-assisted workflows
- Anyone interested in what Claude can create 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
