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👤 Guest Bio
Elie Schoppik leads Technical Education at Anthropic, building Claude Code’s learning-first workflows and Anthropic Academy to teach people how to verify AI outputs (not just consume them).
Before Anthropic, he founded early ed-tech products and Rhythm School, a Bay Area coding bootcamp that trained thousands of developers. Elie’s work sits at the intersection of engineering and pedagogy; designing exercises, evals, and prototype workflows that teach verification, build durable judgment, and make AI safe and useful in the real world.
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🎙Episode Intro
When ChatGPT landed, “fast answers” replaced judgment for many learners, creating a gap between getting an answer and knowing if it’s correct. Elie’s work is about closing that gap: building workflows where AI is the teacher’s assistant, not the oracle. He gives us practical rules, concrete design patterns (examples-first prompts, end-state screenshots, evals), and ready-to-run exercises every team can use to build AI fluency.
⏱ What’s Covered
(02:29) Elie’s background and leading technical education at Anthropic
(03:41) How AI education differs from coding
(07:15) Anthropic’s products: Cloud Code, MCP, and model actions
(17:36) Why examples and context matter in prompting
(18:45) Context engineering: narrowing tasks to get better model output
(19:59) Core topics to study beyond tool use
(32:26) Role of education in helping companies join the “successful 5%”
(35:37) Hackathons as a catalyst for AI adoption in organizations
(44:59) Dopamine hits, quick wins, and why they matter in AI learning
(49:03) Can mastering AI tools reduce “AI anxiety”?
(50:47) Balancing speed vs. responsibility in an AGI timeline
(52:29) Anthropic Academy resources for technical + non-technical teams
(55:17) What excites Elie most about the future of AI learning
(56:03) How parents can prepare kids for an AI-driven future
💡 Key Takeaways
Teach verification, not shortcuts. Build learning modes that require users to answer follow-ups, explain reasoning, or run checks. Don’t let the model hand off the whole answer.
Start with the building blocks. Teach high-level context engineering (MCP, data connectors, subagents) so teams understand tradeoffs even if they never run a server.
Prototype with an end state. Give the model concrete examples or a screenshot of “what good looks like” and iterate. It’s often faster and more reliable than vague instructions.
Ship evals early. Define unit tests/evals for any LLM workflow to avoid brittle deployments and false confidence.
Short sprints beat courses. Run concentrated, hands-on learning + immediate application to capture momentum and build confidence.
Make the loop self-improving. Use Cloud Code + Claude to generate assessments (quizzes), evaluate responses, and retrain the learning flow — that closed loop is a force multiplier for fluency.
Exercises you can run this week
90-minute “Play Sprint” — pick a painful recurring task, give Claude a single “end state” example, and iterate until you get a working prototype. (Use Cloud Code for integrations.)
Eval the evals (45 min) — for one LLM task (summarize customer emails), write 5 unit tests that define success/failure and run them against model outputs. If none exist, you’re building on a house of cards.
Make-a-quiz loop (60 min) — ask Claude to create a short quiz on an internal topic, have Cloud Code assemble context (Slack, Drive snippets), run the quiz against a colleague, and let Claude grade + explain mistakes.
📚 References & resources
Anthropic — Research lab building safer, more helpful LLMs (helps you rely on higher-quality assistant outputs).
Anthropic Academy (courses & labs) — Free hands-on courses and labs to build AI fluency (helps teams get practical, job-ready skills fast).
Model Context Protocol (MCP) — Open standard for wiring models to data/tools (Gmail, Drive, Slack). Powers reliable model ↔ data integrations. (Helps you connect LLMs to real systems safely).
Claude Code / Claude desktop — Agentic coding + Cloud Code tooling for prototyping and executing tasks (helps teams prototype automations and integrate with existing stacks).
Constitutional AI (research paper, 2023) — Training method that uses a “constitution” to make outputs helpful, honest, and harmless (helps you align model behavior to safety goals).
🔗 Where to Find Elie
Twitter/X: @eschoppik
LinkedIn: Elie Schoppik
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Until next time,
Haroon