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Paula Livingstone writing · projects · tools

Projects

The Frontier Engineer Project

Anthropic is investing $100 million to train 10,000 Frontier Deployed Engineers. This project asks whether an experienced industrial cybersecurity engineer can independently build the same underlying capabilities through targeted study, hands-on engineering and the delivery of a real agentic AI system.

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The Frontier Engineer Project › 06 — Evaluation & Acceptance

Develop and Evaluate LLM Features Effectively — Coursera

Defining requirements, acceptance criteria and measurable evidence for deciding whether an LLM-based feature actually solves the problem it was built to address.

Why this is in the programme

The final test of an AI system is not whether it produces impressive outputs, but whether it satisfies a useful requirement sufficiently well to justify its use.

Evaluation therefore needs to begin with the problem rather than the model.

Course

Develop and Evaluate LLM Features Effectively — Coursera

The course covers requirements, product definition, acceptance testing, user acceptance and the use of measurable criteria for evaluating LLM features.

Role in the experiment

This stage will help define the evidence needed to judge the eventual system.

The objective is to finish the twelve-week experiment with more than a subjective judgement that the prototype "works". Its value and limitations should be supported by explicit acceptance criteria and measured results.