Altitude, depth and range
Felix Rieseberg’s framework for ambition with AI models: speed repeats work faster, altitude targets outcomes, depth uses expert instruments and range combines disciplines.
- Author
- Felix Rieseberg
- Published
- 2026-10-13
The directions are separate choices, not a requirement to make every task larger. Altitude changes the purpose, depth changes the method, and range changes the disciplinary coverage.
Resources—observation, context and a test environment—make the request executable. A more ambitious prompt does not itself establish better performance or authorize broader actions.
Connected
- Felix RiesebergSoftware engineer and author of the October 2026 essay How to be more ambitious: A tutorial for humans with models, which describes speed, altitude, depth and range.
- Outcome-based promptingAsking an AI agent to pursue a specified change for a user or system, with evidence and boundaries, instead of merely producing a named deliverable.
- AI outside judgeAn acceptance check outside an agent’s self-assessment, such as a deterministic test, held-out evaluation, measurement or separately briefed reviewer.
- Multidisciplinary agent reviewA workflow in which specialist passes apply distinct methods and produce concrete evidence, rather than merely imitate expert personas.
- Claude CodeAnthropic's agentic coding tool that reads a codebase, edits files, runs commands and verifies its work from the terminal, IDE or web.
Field notes that cover Altitude, depth and range
More concepts & methods
- machine learningscientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions
- deep learningspecialized form of machine learning that uses artificial neural networks to identify patterns in data
- genetic algorithmcompetitive algorithm for searching a problem space
- Prompt injectionAn attempt to make an AI system follow instructions embedded in untrusted material such as email, documents or websites instead of the authorized user’s task.
- Accessibility testingEvaluation of digital accessibility through automated checks, knowledgeable manual assessment and research with people with different access needs. Conformance evaluation and usability research provide complementary evidence.
- AI data retentionThe rules governing which AI inputs, outputs, logs and derived records remain in a system, for what purpose, and for how long. Not using data for training does not mean that it is not stored.