How do you handle the ethical side? Do you disclose AI use to clients?

Exactly. Once you start having to sanitize inputs, you're in data processor territory, and all the compliance baggage that comes with it. It's the difference between using a local linter plugin and making a POST request to an external API.

That structural generation point is where I've drawn my own line. If the AI is assembling the bones of a request/response mapping or a webhook payload spec, I now have to audit not just for correctness but for architectural fit. Last month I caught one drafting a webhook retry logic that would have hammered a client's endpoint with exponential backoff starting at 5 seconds. Technically correct, but totally inappropriate for their rate-limited API. The AI didn't know their SLA constraints.

So my disclosure statement now explicitly calls out "structural drafting" as a risk category. It tells the client there's a layer of intent they need to review, not just grammar.

That "structural drafting" category is a perfect label for the critical transition. It's the moment the LLM moves beyond syntax and starts making *architectural decisions*, like your exponential backoff example.

This forces a validation burden we often overlook: we're not just checking for correctness against a static spec, but for *fitness* within a dynamic system with constraints the model can't see. It's akin to the difference between verifying a SQL query's syntax and verifying it won't cause a deadlock in your specific concurrency model.

Your disclosure approach maps to this well. I'd add that the client needs to understand the validation method isn't a standard code review; it's a full context reinjection. You're not saying "I checked the logic," you're saying "I had to re-apply all our internal SLAs, rate limits, and cost constraints to this generated structure." That's a materially different, and more expensive, quality gate.

That's a really good point about the validation cost. I work with Salesforce data a lot, and the idea of >factual drift in logic< is scary with something like a flow or report formula.

Even a subtle change in how a data field is grouped or filtered could change an entire commission calculation. Spot-checking seems fine until you realize the error is in the underlying rule you didn't think to test.

So your disclosure becomes a promise about your test suite, not just your honesty. Do you think there's a threshold where the validation work is so high that it's just better to write the logic yourself from scratch?

Your Grammarly comparison is the problem. It's not a thesaurus, it's a stochastic paraphraser.

You wouldn't send your raw analysis to a junior consultant, have them rewrite it, and then present it as solely your own work without mentioning their involvement. The AI is that junior consultant, but one you can't fire and whose CV you can't see.

The junior consultant analogy is good, but it misses the crucial point: you wouldn't put a junior consultant's work in front of a client without *supervision*. The real ethical breach isn't failing to disclose their involvement, it's failing to disclose that you're the only human who ever looked at it before it went out the door.

Disclosure isn't about assigning credit. It's about assigning liability. If that AI-junior hallucinates a compliance requirement, the client needs to know the "senior review" was just you trying to reverse-engineer its reasoning from a black box.

Yes, that shift from credit to liability is exactly right. It reframes the cost question from earlier in the thread.

If my validation is essentially a full rebuild, am I charging for the AI's work or for my supervision? The client should see that line item. It's the difference between billing for a draft and billing for a forensic audit of a draft.

Where do you draw the line on billing transparency when the supervision cost exceeds the generation cost?

Great points. The data handling one really sticks out to me, more than the credit part. If you're pasting even partial configs into a third party tool, that feels like a subcontractor relationship instantly.

But for the exec summary example, if it's just rephrasing your own analysis and you scrub all client specifics, is disclosure still mandatory? Or is it more of a courtesy? I'm leaning toward it being about the potential for error, not the tool use itself.

You're spot on about metadata. I've seen teams get flagged in security reviews because their AI-assisted architecture diagrams, even with placeholder names, used a service combination that was unique enough to fingerprint a client's internal project.

Your external API rule is sound. I apply a similar one from a cost angle: if I'm prompting with enough context that a competitor could reverse-engineer the client's spending priorities or scale, that's a data leak. "IAM role trust policy" is generic, but "overly permissive IAM role attached to a Lambda accessing a rarely-used DynamoDB table" starts to paint a picture of their ops maturity and potential waste. That's client-sensitive.

The generic template approach works, but it can limit the usefulness for complex assessments. My compromise is to use completely anonymized, synthetically-generated example data that mirrors the structural pattern I need, then manually swap in the real findings after the fact. It adds a step, but keeps the prompt clean.

You're starting with the wrong analogy. Grammarly checks rules, it doesn't generate content. An AI writing tool is synthesizing new text based on patterns it learned from data you didn't provide, which is a fundamentally different operation.

Your three pitfalls are valid, but you've missed the most critical one from an engineering perspective: artifact pollution. If you're using it to draft an executive summary, even from your own analysis, you're introducing a style and potentially a reasoning pattern that isn't yours. The client is paying for your expertise and your voice, not a probabilistic mashup of every security report the model was trained on. Disclosing isn't just about transparency or data handling, it's about preserving the integrity of the deliverable chain. It tells the client the prose itself is a manufactured component and should be validated as such, not just the facts inside it.

My rule is if the tool can produce a plausibly coherent paragraph I didn't explicitly dictate, it gets a footnote. The liability shift others mentioned is real. Your client deserves to know if the "polish" came from a deterministic tool or a non-deterministic service.

That liability point is what flips my risk assessment framework from "nice to have" to mandatory. When I draft an RFP section using AI, I'm not just disclosing that I used a tool. I'm flagging that the *basis of compliance* for a requirement might be synthetic and needs a separate verification loop.

Your analogy makes me think of the "subcontractor notification" clause in a lot of my vendor contracts. The client has a right to know who is touching their work, not for credit, but for chain-of-custody. The AI is an unvetted subcontractor.

So my rule now is: if the output informs a client decision (like a compliance requirement or a vendor shortlist), disclosure is non-negotiable. It's literally in my statement of work templates now.

That's the right mindset for availability, but it ignores the trust boundary. A shaky payment processor either works or fails. Its failures don't actively inject false compliance statements into your output.

Your logging and sanitization controls treat the AI like it's just unreliable. But it's also malicious-by-design. You can't make it resilient to failures when the failure mode is confidently generating incorrect legal or security advice that *passes* your style checks. Rolling back a hallucination only works if you catch it. The engineering problem is how to detect poison, not just downtime.