The Frameworks
The 4Ds
Four skills for working with AI: deciding what to hand off (Delegation), communicating clearly (Description), evaluating what comes back (Discernment), and using AI responsibly (Diligence).
Why it matters: These are the core competencies the pilot develops in you.
Delegation
Deciding which tasks to give Claude and which to keep for yourself. This is where you start every interaction: does this task benefit from AI assistance, and what part specifically?
Why it matters: Smart delegation multiplies your time without sacrificing quality or control.
Description
How clearly you communicate what you need from Claude, including what you want, how you want it done, and what tone or format to use.
Why it matters: Better descriptions produce better outputs on the first try, which saves you revision time.
Discernment
Your ability to evaluate whether Claude's output is accurate, appropriate, and ready to use. This is the skill that prevents errors from reaching students, funders, or the board.
Why it matters: You're the gatekeeper. Your judgment is what makes AI safe in your organization.
Diligence
Using AI responsibly: being transparent about AI's role in your work, following governance rules, and owning every output you send.
Why it matters: Your name goes on it, so you verify it. Diligence protects your credibility and the organization's reputation.
Automation
When you give Claude a specific task with clear instructions and it executes. Think: 'Reformat this list into a table' or 'Summarize these meeting notes.' You define the task; Claude does it.
Why it matters: Automation handles the routine, freeing you for higher-impact work.
Augmentation
When you and Claude think together, going back and forth to develop something. Think: brainstorming grant angles, refining a draft through multiple rounds, or exploring different framings for a board presentation. You both contribute.
Why it matters: Augmentation is where the magic happens—Claude amplifies your thinking, not replaces it.
Agency
When you set Claude up with background knowledge and guidelines so it can work more independently. Think: Claude Projects loaded with DC CAP context, or Skills that encode your team's standards. You configure the system; Claude operates within it.
Why it matters: Agency scales your impact—Claude remembers your context and priorities across conversations.
Technical Terms You'll Hear
Prompt
The message or instruction you type to Claude. Everything you write in the chat box is your prompt, including questions, instructions, and any documents you paste in.
Why it matters: Your prompt is how you talk to Claude. The better your prompt, the better your result.
Prompt Engineering
The practice of writing prompts that get you better results. DC CAP uses the Context-Task-Content-Constraints template to structure prompts.
Why it matters: Good prompt engineering means fewer revision rounds and faster results.
Iteration
Revising your prompt or pushing back on Claude's output to get a better result. Research shows this is the single strongest predictor of AI fluency.
Why it matters: The more you iterate, the faster your skills grow. Every revision teaches Claude what you need.
Context Window
The amount of text Claude can hold in its memory during a single conversation. Think of it as Claude's working memory. If a conversation gets very long, Claude may lose track of things mentioned early on.
Why it matters: You don't need to manage it, but understanding it helps you know when to start fresh or save important context.
Hallucination
When Claude states something confidently that sounds plausible but is factually wrong. This happens because Claude generates language patterns, not truth.
Why it matters: Always verify statistics, citations, and specific claims before using them. This is where Discernment saves the day.
Token
A small chunk of text (roughly one word) that Claude processes. You don't need to manage tokens yourself.
Why it matters: Just know that longer conversations and documents use more of Claude's working memory. It helps explain why very long chats can slow down.
Model
The AI system itself. Claude is a model built by Anthropic. Different models have different capabilities. DC CAP uses Claude, specifically the enterprise version with data protections.
Why it matters: When someone says 'model,' they mean the AI engine you're talking to. It's helpful to know what model you're using and its strengths.
System Prompt / Project Instructions
Background instructions that shape how Claude responds to you, set before the conversation starts. DC CAP's organizational context, brand voice rules, and governance policies live in Project Instructions so every team member gets consistent, context-rich outputs.
Why it matters: These are the rules and guardrails that make Claude behave like "DC CAP's Claude," not generic Claude.
Project (in Claude)
A workspace in Claude where you can load documents and instructions that persist across conversations. DC CAP's Intelligence Project contains our impact data, pitch deck, and strategic documents so Claude has organizational context whenever you need it.
Why it matters: Projects eliminate the need to re-explain context every time. Claude remembers your organization's priorities and materials.
Skill (in Claude)
A specialized instruction set that tells Claude how to do a specific task well. DC CAP has built Skills for grant writing, student outreach, data interpretation, and more. Skills encode your team's best practices so Claude follows them every time.
Why it matters: Skills make your team's expertise repeatable and consistent. Once you build a Skill, anyone can use it—no training needed.