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S2 · AI Evaluation & Reliability · L0 FREE

LLM Failure Pattern Scan

Stop trusting one good answer: repeat a small trial and see which of five failure patterns show up.

Best for: Engineers about to evaluate an LLM feature who are not sure where to start.

Free includes L0 of S2 · AI Evaluation & Reliability

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S2 · AI Evaluation & Reliability L0 FREE: LLM Failure Pattern Scan, product artwork

You walk away with

  • A report (JSON, Markdown and standalone HTML) built from the sanitized trials you record.
  • The exact variation between repeated outputs, stated as a measured figure.
  • The five failure patterns named in the guide: intermittent acceptance failure, context-condition failure, unsupported material claim, output-contract failure and system-version regression.
  • The checks you declared run, and the report proposes one follow-up check.
  • An 18-page guide, English and Polish guidebooks, a blank input template and 23 automated tests.

What the workflow does

Record the configuration, repeat a small sanitized trial, classify the observation, and choose one follow-up check.

Bring

  • recorded prompt and configuration
  • sanitized outputs
  • declared risk context

Leave with

  • configuration record
  • pattern observation
  • follow-up check
  • limits

What is in the package

Includes L0 FREE: LLM Failure Pattern Scan (S2 · AI Evaluation & Reliability).

You need: Node.js 22 and a terminal.

Each package is a working workflow with tested code, a runnable example, recovery steps and an HTML report, plus an English and Polish guidebook that explains the method. The workflow is the product; the guidebook supports it.

Prices are cumulative: each price covers that level and every level below it, so the catalog shows no sums or savings.

What the guidebook covers

  • intermittent acceptance failures
  • context-condition failures
  • unsupported material claims
  • output-contract failures
  • system-version regressions

The guidebook extends the workflow with the reasoning and adaptation guidance. It is not evidence that a result has already run in your environment.

Use it safely

Use sanitized inputs inside a declared boundary and review every proposed action. The workflow stops when evidence, authority or an owner decision is missing.

The included code runs on your machine, makes no model calls and needs no AI tool. If you want an assistant to help draft the input, the onboarding guide shows how; you review the result before anything is acted on.

Next level

L1 START, Initial LLM Test Checklist, turns a promising demo into five repeatable checks with the variation and the risks written down.

See L1 START: Initial LLM Test Checklist · Open it in our store · See all five levels

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