Section 129 · Chapter 16, Introspection: White-Box Testing Networks
Neural Architecture as a Test Surface
A transformer is a chain of internal transformations, and each transformation can become a source of observability evidence.
observabilitytokenizationattentionneural architecture test surface
What to do
- Save the raw input, prompt template, assembled prompt, tokenizer output, truncation boundary, retrieved context, model version, parameter settings, tool schema, tool calls, tool results, output parser result, safety or policy decision, final response, and trace id.
- Define runnable checks that exercise observability, tokenization, and attention.
- Set acceptable outcomes and blocker failures for observability, tokenization, and attention before running the evaluation.
Evidence to preserve
- Save the raw input, prompt template, assembled prompt, tokenizer output, truncation boundary, retrieved context, model version, parameter settings, tool schema, tool calls, tool results, output parser result, safety or policy decision, final response, and trace id.
- Preserve the inputs, versions, configurations, raw outcomes, and results for observability, tokenization, attention, neural architecture test surface needed to reproduce work on Neural Architecture as a Test Surface.
- Report results for observability, tokenization, attention, neural architecture test surface by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, map each architecture block to observability: tokenizer, prompt assembler, embeddings, layer activations, attention, MLPs, logits, sampler, tool call, parser, and safety layer. Each block needs versioning and failure labels.
Continue the conversation
Apply this to your context.
Save your product context once, then open a focused conversation that combines it with this concept.
Cite this page
Jason Arbon. "Neural Architecture as a Test Surface." Testing AI Knowledge Edition, section 129.
https://jarbon.ai/testing-ai/knowledge/ch129-neural-architecture-test-surface.html