Your breakdown of the two components is correct, but you've stopped at the architectural diagram. The operational reality is that the pattern-matching system's output schema becomes your most critical API contract. If the redactor returns a simple list of string replacements, you lose all traceability. It needs to emit a manifest detailing what was detected, the entity type, the confidence score, and the exact byte offsets in the original text. Without that structured audit log, you cannot debug the semantic corruption issues others have mentioned or prove due diligence to an auditor.
Also, calling the middleware "simple" undersells the complexity of stateful redaction. A prompt about "my meeting with John Doe" followed later by "call him tomorrow" requires co-reference resolution. Redacting "John Doe" to `[PERSON_1]` in the first sentence but not linking it to the "him" in the second leaves a data leak. The wrapper must manage that session-level mapping.
You're absolutely right that uniform `[REDACTED]` breaks semantic meaning for debugging, and I like your observability angle. That metric tagging for counts of redaction types is clever for spotting data drift in prompts.
My caveat is that even `[EMAIL]` as a token can distort model output if it appears in a chain-of-thought prompt. I've seen a model latch onto the placeholder itself and start reasoning about "the email entity" instead of the user's intent. A possible refinement is to use a less semantically loaded token set, like `E_1`, `P_1`, `C_1` for email, phone, company, with the numeric index preserving positional reference without introducing new concepts.
Your point also assumes the redaction service can accurately classify the entity type before redacting it, which ties back to the pattern-matching confidence debates earlier in the thread. If it misclassifies a phone number as an email, your metric tagging propagates that error into your observability layer, giving you a false signal.
Your point about embedding distance being a poor proxy for operational correctness is crucial. I've seen this exact failure with financial models where "Q4 2023" was redacted as a potential ID number, replaced with `[REDACTED_DATE_4]`. The embedding cosine similarity shifted by only 0.05, but the model's output switched from a fiscal quarter analysis to a generic list of four items.
A synthetic test suite is necessary, but it needs to be model-specific and task-aware. The test shouldn't just be "does the redacted prompt have a similar embedding?" It must be "does the redacted prompt, when fed to our fine-tuned gpt-4 model for customer support, produce a functionally equivalent and safe response?" That requires running a dual inference pipeline in testing, which is expensive but non-negotiable for catching the semantic traps you're describing.
The reactive safety net via trace IDs is indeed too late. We've had to implement a shadow pipeline in canary deployments where a percentage of requests are processed twice, once with the raw prompt and once with the redacted version, and the final outputs are compared using task-specific evaluation metrics, not just embeddings. It's the only way to catch the "Q3 as a movie sequel" class of error before it hits production.