AI Researcher

The Andrew Ng Framework

Applied AI & Pragmatic Deployment

IncludedNative Claude Skill + plain .md for ChatGPT, Gemini & every LLM
641 frameworks in the catalogDelivered as Claude Skill + plain .mdReviewed Sep 22, 2026Instruction-only · verifiable

Andrew Ng represents one of the most distinctive thinking patterns in their field. Andrew Ng's approach, captured in this framework as mental models, decision heuristics, and the signature questions they would ask.

The applied ai & pragmatic deployment framework distils Andrew Ng's documented approach into a .md skill file you can load into Claude, ChatGPT, or any LLM in seconds.

Where most AI personas generate generic output, this framework is grounded in real documented work: interviews, writing, published frameworks, and the specific decisions Andrew Ng made when it mattered. Use it for decision-making, writing, strategy, or any situation where you want your AI to think with Andrew Ng's distinctive patterns rather than a default model voice.

Inside the framework

A glimpse of Andrew Ng's thinking.

Core Philosophy

AI's transformative value is realized through disciplined iteration on real data, not through waiting for perfect algorithms. The bottleneck in most organizations is not model sophistication but the absence of a systematic process for identifying high-value, technically feasible AI opportunities and executing against them quickly. Concrete, scoped projects compound into organizational AI capability faster than any single moonshot.

Five Mental Models
  1. 01
  2. 02
  3. 03
  4. 04
  5. 05

Five specific models, each named and explained with concrete examples, unlocked with purchase.

A Signature Question · sample

"What does the training error tell us, and what does the gap between training error and dev error tell us separately?"

4 more questions in Andrew Ng's voice are unlocked with purchase.

What Andrew Ng Would NOT Do · sample
✗**Would not greenlight a project based on a benchmark accuracy number alone.** Ng is explicit that a model performing at 98% accuracy sounds impressive until you learn that human-level performance on the same task is 99.5%, which means the model is still making twice the errors a human would and may be unfit for deployment.

Full list of anti-patterns unlocked with purchase.

Continue reading

The full framework adds: detailed explanations of all five mental models with real-world examples, the decision heuristics Andrew Ng actually used, remaining signature questions, the complete list of anti-patterns, a copy-paste activation prompt, and a worked example.

Unlocked the moment you purchase. ~2,000 words total. Delivered as a Claude Skill and plain .md within 60 seconds of checkout.

What's inside
  • ✦Core philosophy: 2–3 sentences grounded in Andrew Ng's documented worldview
  • ✦Five signature mental models: each named, with concrete examples from their work
  • ✦Decision heuristics: the concrete rules they actually used
  • ✦Signature questions: five questions that sound like Andrew Ng, not a generic MBA
  • ✦What they would NOT do: the anti-patterns this framework rejects
  • ✦Copy-paste activation prompt: drop straight into Claude or ChatGPT
  • ✦Worked example: the framework applied to a realistic scenario
Delivered in two formats
  • →andrew-ng.zip: a native Claude Skill. Upload to Settings → Skills in Claude, or unzip to ~/.claude/skills/
  • →andrew-ng-framework.md: plain markdown. Works in ChatGPT Custom GPTs, Gemini, Claude Projects, agent system prompts, anywhere
Sample output

What it does with a real question.

The brief

A founder is deciding whether to take a large but difficult client.

Take it, but only as a scoped pilot, not a full commitment. The size of the contract is the least interesting fact here. What matters is whether this client can generate proof points and learning faster than it consumes your team's attention. Large, difficult clients tend to fail in one of two ways: they demand custom work that never generalizes, or they stretch a small team so thin that every other customer suffers. Both are survivable if you cap the exposure early.

Think of it through the data flywheel. A hard client is valuable if the difficulty is the kind that makes your product better for everyone else: messy inputs, strict requirements, real usage at scale. It is poison if the difficulty is political, such as shifting scope, slow approvals, and demands that only apply to them. You can usually tell which one you are facing by how they behave in the first two weeks of negotiation.

So propose a six-month engagement with one narrowly defined deliverable, a measurable success metric agreed in writing, and a cap on custom work, say no more than 25 percent of engineering time. If they push back hard on that structure, you have learned what the full relationship would have been, and you have lost nothing.

Your next move this week: write a one-page scope that names the single outcome they care most about and the dollar value of achieving it, then bring it to them before you discuss price.

The full framework carries five models, decision heuristics, signature questions and a worked example.

Written by claude-sonnet-5-5 with this framework loaded, 2026-10-11. Checked automatically for length and for copied framework text.

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Works as a Claude Skill (.zip) and as a plain .md for ChatGPT, Gemini & every LLM
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Inspired by the documented thinking of Andrew Ng. A mental-model toolkit, not a literal representation. Delivered as a .md file compatible with Claude, ChatGPT, Gemini, and any LLM.

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