Claude Fundamentals & AI Work Modes

8 min

Why Learn Claude

This course isn't about memorizing where a button lives. Interfaces change, but knowing which tasks to delegate to AI, how to give clear instructions, and how to review results are durable skills.

• Goal: build a transferable AI work mode, not muscle memory for one UI
• Scope: three primary work modes — Chat, Cowork, Code
• Principle: Human → AI → Human. A human defines the task and stays accountable for the result

Key takeaway: This course teaches work modes and judgment, not a specific UI.

10 min

What Is Generative AI

Generative AI produces new content — text, summaries, code, draft analysis — from what you give it (text, documents, images). Its core ability is generating a plausible continuation from context.

Key takeaway: Generative AI is good at producing new content, but it does not inherently know right from wrong.

8 min

Limits of Generative AI

• It can produce plausible-sounding but incorrect content (hallucination)
• It does not verify sources unless explicitly asked and given evidence
• Its grasp of recent or internal information depends on the context you provide
• It can't take real-world action on its own unless paired with tools or agentic features

Key takeaway: Generative AI's output needs human verification, especially for facts and numbers.

8 min

What Is Agentic AI

Agentic AI does more than generate content — it plans steps, calls tools, executes multi-step tasks, and adjusts based on intermediate results. Claude's Cowork and Code capabilities fall into this category.

Key takeaway: Agentic AI plans and executes multiple steps, not just a single reply.

8 min

Generative vs Agentic

Key takeaway: Choose generative or agentic based on whether the task needs autonomous multi-step execution.

8 min

AI Autonomy and Governance

More agentic capability means more autonomy — and more need for governance. Before delegating a task, decide the scope of authority, what actions are allowed, and who reviews the result.

• Authorization scope must be explicit: what AI can and cannot do
• High-impact outputs (external documents, decision inputs) always need human review
• Keep a traceable record for later inspection

Key takeaway: More autonomy demands stronger governance and review.

8 min

Claude's Working Mental Model

Think of Claude as three work modes: Chat (thinking together), Cowork (delegating multi-step tasks), and Code (automation). All three share the same judgment and review principles.

Key takeaway: Chat, Cowork, and Code are three expressions of the same work mode.

6 min

Chat: Think, Ask, Draft

Chat is best for quick discussion, brainstorming, drafting, and clarifying a problem — a single turn or a short back-and-forth.

Key takeaway: Chat suits single-turn or short multi-turn thinking and drafting work.

6 min

Cowork: Delegate and Execute

Cowork is best for delegating a fairly complete task — Claude reads multiple sources, plans the steps, and produces a result for you to review and adjust.

Key takeaway: Cowork suits multi-step tasks that pull from several sources.

6 min

Code: Build and Automate

Code is best for repetitive, well-defined tasks — batch data cleanup, generating a report script. A one-off task rarely needs code; a recurring task is worth automating.

Key takeaway: Only recurring, well-defined tasks are worth automating.

6 min

Projects: Centralizing Long-Running Context

Projects let you gather related documents, instructions, and style guidance in one place, so every conversation continues from the same context without re-pasting material.

Key takeaway: Projects centralize context you reuse across conversations.

6 min

Artifacts: Editable, Reusable Output

Artifacts are standalone, continuously editable outputs — a document, a table, a simple web page — suited to content that needs iteration before it is delivered.

Key takeaway: Artifacts suit content that needs iteration and final delivery.

8 min

Tool Selection: Chat, Cowork, or Code?

1. 1Is the task single-turn and easy to state up front?
   Yes → lean toward Chat
2. 2Does it need to combine multiple sources and steps?
   Yes → lean toward Cowork
3. 3Will it recur with well-defined rules?
   Yes → lean toward Code / automation
4. 4Is the task high-risk or high-impact?
   Yes → whichever mode you pick, add stronger human review

Key takeaway: Use "single-turn vs multi-step vs recurring rules" as a quick decision guide.

8 min

Human–AI Review, in Practice

• Separate Fact, Analysis, and Unknown
• Trace key numbers and conclusions back to the source data
• Check for logical leaps or unverified inferences
• Confirm the output still matches the original task goal

Key takeaway: Fact / Analysis / Unknown is the simplest way to review output.

8 min

Four Data-Safety Questions

1. 1Can this data leave internal systems?
   Check data classification and company policy
2. 2Does it contain customer PII or confidential material?
   If so, de-identify first or get authorization
3. 3Who can see this conversation?
   Confirm sharing scope and retention
4. 4What's the cost of getting it wrong?
   Assess risk level and set the review bar accordingly

Key takeaway: Before using AI, confirm the data is allowed to leave internal systems.

6 min

Module 1 Recap

• Generative AI produces content; agentic AI plans and executes multi-step tasks
• Chat / Cowork / Code map to different task shapes
• Projects / Artifacts help manage context and reusable output
• Human → AI → Human: a human defines the task, AI executes, a human reviews and stays accountable

Key takeaway: You're ready for Module 2: hands-on practice.