[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fiX2_H7IkU0wNlIqfoJntLbi8I6WG4kIMkKe8CQZplxQ":3},{"item":4},{"id":5,"idKnowledge":6,"idDomain":7,"idCluster":8,"kindOverride":9,"slug":10,"title":11,"description":12,"bodyMarkdown":13,"bodyHtml":14,"author":15,"date":16,"createdAt":17,"topics":18,"image":26,"hasDownload":27,"fileName":28,"youtubeId":29,"domainCrumb":30,"clusterCrumb":33},"320","8E70B86B-F78A-214A-BF17-83FC7E3ECE1E","8F2761C8-348C-C649-BC16-18822CE2D198","592F5828-A0A3-B740-9B42-F2CD1BCE04DE","article","how-to-evaluate-an-ai-generated-feature","How to evaluate an AI-generated feature","A practical checklist for judging whether an AI-generated feature in your business software is actually reliable enough to ship and trust.","Someone on your team just tried an AI coding assistant and it spat out a working feature in twenty minutes — a script that auto-categorizes invoices, a layout that summarizes customer notes, a button that drafts an email reply. It looks impressive in the demo. But nobody has asked the harder question yet: is it actually correct, is it safe to run against real customer data, and will it still work in six months when the underlying model changes?\n\nThis is the situation more and more business owners, in-house developers, and IT managers are facing right now, whether the AI feature was built with a general-purpose tool or, increasingly, generated directly inside a platform like FileMaker using something like Claude (\"Klai\"-style AI assistants) or low-code AI layout generators such as FmBetterForms. The code compiles, the demo runs, everyone nods — and then it goes live without anyone really evaluating it. This article gives you a concrete, repeatable way to evaluate any AI-generated feature before it touches production.\n\n## Why can't you just test an AI-generated feature like normal code?\n\nBecause it fails differently. Traditional custom-built code usually breaks loudly — a missing field throws an error, a broken relationship shows blank data, a bug is reproducible. AI-generated code tends to fail *quietly and plausibly*. It produces an output that looks right, reads right, and is subtly wrong.\n\nA concrete example: an AI assistant asked to write a FileMaker script that flags overdue invoices might get the date comparison logic almost right — except it compares against the system clock of the client machine instead of the server, so remote users in a different time zone start seeing invoices flagged a day early or late. Nothing crashes. No error log entry. Someone just notices three weeks later that the aging report doesn't match the accountant's numbers.\n\nThat's the core risk: AI-generated features shift the failure mode from \"visible bug\" to \"silent inaccuracy.\" Your evaluation process has to be built around catching that, not just around checking that the feature runs.\n\n## What should you actually check before accepting an AI-generated feature?\n\nUse this as a working checklist. Don't skip steps because the demo looked convincing — the demo is exactly the scenario the AI was most likely to get right.\n\n1. **Does it use real production-shaped data, not the happy-path example?**\n   Test it against a customer record with missing fields, a foreign character in a name, a zero-value invoice, a duplicate entry. AI-generated logic is often trained on clean examples and stumbles on the messy edge cases your actual database is full of.\n\n2. **Can you explain, in plain language, exactly what it does?**\n   If the developer reviewing it can't walk through the logic line by line and explain why each step exists, that's a red flag — not because AI code is inherently unreadable, but because unreviewed code you can't explain is code nobody can maintain or debug later.\n\n3. **What happens when it's wrong?**\n   Does a wrong AI-generated calculation silently update a record, or does it flag itself for human review first? Features that write directly to your database need a materially higher bar of scrutiny than features that merely suggest or summarize.\n\n4. **Is the output reversible?**\n   If an AI-generated script auto-merges two customer records or auto-categorizes 10,000 transactions, can you undo it? Build in a dry-run mode or an audit log before the feature is allowed to touch live data at scale.\n\n5. **Does it depend on an external AI service staying available and unchanged?**\n   A feature that calls out to a large language model for every request introduces a new kind of fragility: model updates, rate limits, API pricing changes, or outages can break a feature that was working fine yesterday. Know exactly where that dependency lives.\n\n6. **Would a human expert in the domain sign off on the logic?**\n   Not just \"does it run\" — would your accountant sign off on the invoice-aging logic? Would your warehouse manager sign off on the reorder-point calculation? AI-generated features often get the *shape* of business logic right and the *specifics* wrong, and only a domain expert catches that.\n\n7. **Is there a test case that would prove it's broken, and have you run it?**\n   Write down, before testing, what a failure would look like. If you can't articulate a failure condition in advance, you're not really testing — you're just watching it run once and hoping.\n\n## How is evaluating AI-generated code different from evaluating a junior developer's code?\n\nIt's a useful comparison, but not a perfect one. A junior developer's code has a person behind it who can explain their reasoning, who learns from feedback, and who will (usually) flag their own uncertainty — \"I wasn't sure about this part.\" An AI code generator has none of that. It will describe its own wrong answer with exactly the same confidence as its right one.\n\nThat means the review burden is actually higher for AI-generated features, not lower, even though the code often *looks* cleaner and more idiomatic than a junior's first attempt. Treat AI output the way you'd treat code from a contractor you've never worked with before: assume competence in syntax, assume nothing about correctness for your specific business rules.\n\n\u003Cimg src=\"\u002Fapi\u002Fknowledge\u002Finline-image\u002F212?w=700&f=webp\" alt=\"checklist next to AI-generated code with a magnifying glass\" loading=\"lazy\" class=\"w-full sm:w-1\u002F3 sm:float-left sm:mr-7 mb-5 rounded-2xl border border-[#E8E8ED] bg-[#F5F5F7]\" \u002F>\n\n## What's different when the AI generates a whole layout or UI, not just a script?\n\nTools that generate entire layouts or interface components — for instance AI-assisted layout builders inside FileMaker such as FmBetterForms — introduce a second evaluation dimension beyond logic: usability and consistency.\n\nAsk:\n\n- Does the generated layout follow the same navigation patterns, field naming, and visual language as the rest of your system, or does it introduce a one-off style that confuses users switching between screens?\n- Does it handle permission levels correctly — will a restricted-access user see a button that, when clicked, errors out because they don't actually have rights to the underlying action?\n- Does it scale to real data volumes? A generated list view that looks fine with 20 sample rows can become unusably slow with 20,000 real ones if the AI didn't apply the indexing or pagination approach your system actually needs.\n\n## Should you evaluate differently depending on how much the feature touches?\n\nYes — match your scrutiny to the blast radius. A simple rule of thumb:\n\n- **Read-only, suggestion-only features** (an AI-generated summary, a draft email a human still has to send): lighter review, spot-check accuracy periodically.\n- **Features that write to your database but only for one user's own records**: moderate review, test against edge cases, keep an audit trail.\n- **Features that write across many records, trigger external systems, or touch financial\u002Fcompliance data**: full review as above, staged rollout, dry-run mode, and sign-off from a domain expert before going live.\n\nThis is the same proportionality principle behind [necessity-driven development](https:\u002F\u002Floggix.com\u002Fen\u002Fblog\u002Fnecessity-driven-development-start-with-what-must-exist): you don't build — or in this case, accept — more than the situation actually calls for, and you invest your scrutiny where the real risk lives instead of spreading it evenly.\n\n## What's a simple sign-off process you can actually use?\n\nMost companies don't need a heavyweight AI governance policy — they need a habit. Before any AI-generated feature goes live, have one person (developer or IT manager) answer these four questions in writing, even briefly:\n\n1. What data did I test this against, and did it include edge cases?\n2. What happens if this feature produces a wrong answer — is it visible, and is it reversible?\n3. Who outside of me (a domain expert) reviewed the actual business logic, not just the demo?\n4. What's the plan if the underlying AI service changes or becomes unavailable?\n\nIf those four answers exist somewhere — a comment in the script, a line in a deployment note, a Slack message — you have a real evaluation. If they don't exist anywhere, you have a demo that got promoted to production by accident.\n\n## FAQ\n\n**Can AI-generated FileMaker scripts be trusted for financial calculations?**\nOnly after the same rigor as manually-written financial code: edge-case testing, a domain expert sign-off, and an audit trail. AI can draft the first version faster, but it doesn't remove the need for review — if anything it makes review more important because the code looks finished sooner.\n\n**Does using an AI assistant inside FileMaker (like Claude-based tools) change who owns the bugs?**\nNo. Whoever accepts and deploys the code owns the responsibility for it, regardless of who or what wrote it. \"The AI wrote it\" is not a valid answer to \"why did this feature corrupt three records.\"\n\n**How do you know if an AI-generated feature will still work after a model or platform update?**\nYou generally don't know for certain — which is why features with a live AI dependency need monitoring and a fallback plan, not just an initial test pass.\n\n**Is it faster overall to use AI-generated features despite the extra review?**\nOften yes, especially for first drafts, repetitive scripting, or generating layout scaffolding — but only if the review step is actually built into your process rather than skipped under deadline pressure.\n\nEvaluating an AI-generated feature properly takes real discipline, and it's exactly the kind of judgment call that benefits from a second set of experienced eyes. Loggix helps teams build that discipline into their FileMaker systems and connected ERP or API workflows — whether that means reviewing and hardening an AI-generated script before it goes live, building the audit trails and permission structures a higher-risk feature needs, or simply sitting down together to map out which AI-assisted shortcuts are actually safe for your business and which ones need a human in the loop.","\u003Cp>Someone on your team just tried an AI coding assistant and it spat out a working feature in twenty minutes — a script that auto-categorizes invoices, a layout that summarizes customer notes, a button that drafts an email reply. It looks impressive in the demo. But nobody has asked the harder question yet: is it actually correct, is it safe to run against real customer data, and will it still work in six months when the underlying model changes?\u003C\u002Fp>\n\u003Cp>This is the situation more and more business owners, in-house developers, and IT managers are facing right now, whether the AI feature was built with a general-purpose tool or, increasingly, generated directly inside a platform like FileMaker using something like Claude ("Klai"-style AI assistants) or low-code AI layout generators such as FmBetterForms. The code compiles, the demo runs, everyone nods — and then it goes live without anyone really evaluating it. This article gives you a concrete, repeatable way to evaluate any AI-generated feature before it touches production.\u003C\u002Fp>\n\u003Ch2>Why can't you just test an AI-generated feature like normal code?\u003C\u002Fh2>\n\u003Cp>Because it fails differently. Traditional custom-built code usually breaks loudly — a missing field throws an error, a broken relationship shows blank data, a bug is reproducible. AI-generated code tends to fail \u003Cem>quietly and plausibly\u003C\u002Fem>. It produces an output that looks right, reads right, and is subtly wrong.\u003C\u002Fp>\n\u003Cp>A concrete example: an AI assistant asked to write a FileMaker script that flags overdue invoices might get the date comparison logic almost right — except it compares against the system clock of the client machine instead of the server, so remote users in a different time zone start seeing invoices flagged a day early or late. Nothing crashes. No error log entry. Someone just notices three weeks later that the aging report doesn't match the accountant's numbers.\u003C\u002Fp>\n\u003Cp>That's the core risk: AI-generated features shift the failure mode from "visible bug" to "silent inaccuracy." Your evaluation process has to be built around catching that, not just around checking that the feature runs.\u003C\u002Fp>\n\u003Ch2>What should you actually check before accepting an AI-generated feature?\u003C\u002Fh2>\n\u003Cp>Use this as a working checklist. Don't skip steps because the demo looked convincing — the demo is exactly the scenario the AI was most likely to get right.\u003C\u002Fp>\n\u003Col>\n\u003Cli>\u003Cp>\u003Cstrong>Does it use real production-shaped data, not the happy-path example?\u003C\u002Fstrong>\nTest it against a customer record with missing fields, a foreign character in a name, a zero-value invoice, a duplicate entry. AI-generated logic is often trained on clean examples and stumbles on the messy edge cases your actual database is full of.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>Can you explain, in plain language, exactly what it does?\u003C\u002Fstrong>\nIf the developer reviewing it can't walk through the logic line by line and explain why each step exists, that's a red flag — not because AI code is inherently unreadable, but because unreviewed code you can't explain is code nobody can maintain or debug later.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>What happens when it's wrong?\u003C\u002Fstrong>\nDoes a wrong AI-generated calculation silently update a record, or does it flag itself for human review first? Features that write directly to your database need a materially higher bar of scrutiny than features that merely suggest or summarize.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>Is the output reversible?\u003C\u002Fstrong>\nIf an AI-generated script auto-merges two customer records or auto-categorizes 10,000 transactions, can you undo it? Build in a dry-run mode or an audit log before the feature is allowed to touch live data at scale.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>Does it depend on an external AI service staying available and unchanged?\u003C\u002Fstrong>\nA feature that calls out to a large language model for every request introduces a new kind of fragility: model updates, rate limits, API pricing changes, or outages can break a feature that was working fine yesterday. Know exactly where that dependency lives.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>Would a human expert in the domain sign off on the logic?\u003C\u002Fstrong>\nNot just "does it run" — would your accountant sign off on the invoice-aging logic? Would your warehouse manager sign off on the reorder-point calculation? AI-generated features often get the \u003Cem>shape\u003C\u002Fem> of business logic right and the \u003Cem>specifics\u003C\u002Fem> wrong, and only a domain expert catches that.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003Cli>\u003Cp>\u003Cstrong>Is there a test case that would prove it's broken, and have you run it?\u003C\u002Fstrong>\nWrite down, before testing, what a failure would look like. If you can't articulate a failure condition in advance, you're not really testing — you're just watching it run once and hoping.\u003C\u002Fp>\n\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2>How is evaluating AI-generated code different from evaluating a junior developer's code?\u003C\u002Fh2>\n\u003Cp>It's a useful comparison, but not a perfect one. A junior developer's code has a person behind it who can explain their reasoning, who learns from feedback, and who will (usually) flag their own uncertainty — "I wasn't sure about this part." An AI code generator has none of that. It will describe its own wrong answer with exactly the same confidence as its right one.\u003C\u002Fp>\n\u003Cp>That means the review burden is actually higher for AI-generated features, not lower, even though the code often \u003Cem>looks\u003C\u002Fem> cleaner and more idiomatic than a junior's first attempt. Treat AI output the way you'd treat code from a contractor you've never worked with before: assume competence in syntax, assume nothing about correctness for your specific business rules.\u003C\u002Fp>\n\u003Cimg src=\"\u002Fapi\u002Fknowledge\u002Finline-image\u002F212?w=700&f=webp\" alt=\"checklist next to AI-generated code with a magnifying glass\" loading=\"lazy\" class=\"w-full sm:w-1\u002F3 sm:float-left sm:mr-7 mb-5 rounded-2xl border border-[#E8E8ED] bg-[#F5F5F7]\" \u002F>\n\n\u003Ch2>What's different when the AI generates a whole layout or UI, not just a script?\u003C\u002Fh2>\n\u003Cp>Tools that generate entire layouts or interface components — for instance AI-assisted layout builders inside FileMaker such as FmBetterForms — introduce a second evaluation dimension beyond logic: usability and consistency.\u003C\u002Fp>\n\u003Cp>Ask:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Does the generated layout follow the same navigation patterns, field naming, and visual language as the rest of your system, or does it introduce a one-off style that confuses users switching between screens?\u003C\u002Fli>\n\u003Cli>Does it handle permission levels correctly — will a restricted-access user see a button that, when clicked, errors out because they don't actually have rights to the underlying action?\u003C\u002Fli>\n\u003Cli>Does it scale to real data volumes? A generated list view that looks fine with 20 sample rows can become unusably slow with 20,000 real ones if the AI didn't apply the indexing or pagination approach your system actually needs.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Should you evaluate differently depending on how much the feature touches?\u003C\u002Fh2>\n\u003Cp>Yes — match your scrutiny to the blast radius. A simple rule of thumb:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Read-only, suggestion-only features\u003C\u002Fstrong> (an AI-generated summary, a draft email a human still has to send): lighter review, spot-check accuracy periodically.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Features that write to your database but only for one user's own records\u003C\u002Fstrong>: moderate review, test against edge cases, keep an audit trail.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Features that write across many records, trigger external systems, or touch financial\u002Fcompliance data\u003C\u002Fstrong>: full review as above, staged rollout, dry-run mode, and sign-off from a domain expert before going live.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This is the same proportionality principle behind \u003Ca href=\"https:\u002F\u002Floggix.com\u002Fen\u002Fblog\u002Fnecessity-driven-development-start-with-what-must-exist\">necessity-driven development\u003C\u002Fa>: you don't build — or in this case, accept — more than the situation actually calls for, and you invest your scrutiny where the real risk lives instead of spreading it evenly.\u003C\u002Fp>\n\u003Ch2>What's a simple sign-off process you can actually use?\u003C\u002Fh2>\n\u003Cp>Most companies don't need a heavyweight AI governance policy — they need a habit. Before any AI-generated feature goes live, have one person (developer or IT manager) answer these four questions in writing, even briefly:\u003C\u002Fp>\n\u003Col>\n\u003Cli>What data did I test this against, and did it include edge cases?\u003C\u002Fli>\n\u003Cli>What happens if this feature produces a wrong answer — is it visible, and is it reversible?\u003C\u002Fli>\n\u003Cli>Who outside of me (a domain expert) reviewed the actual business logic, not just the demo?\u003C\u002Fli>\n\u003Cli>What's the plan if the underlying AI service changes or becomes unavailable?\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cp>If those four answers exist somewhere — a comment in the script, a line in a deployment note, a Slack message — you have a real evaluation. If they don't exist anywhere, you have a demo that got promoted to production by accident.\u003C\u002Fp>\n\u003Ch2>FAQ\u003C\u002Fh2>\n\u003Cp>\u003Cstrong>Can AI-generated FileMaker scripts be trusted for financial calculations?\u003C\u002Fstrong>\nOnly after the same rigor as manually-written financial code: edge-case testing, a domain expert sign-off, and an audit trail. AI can draft the first version faster, but it doesn't remove the need for review — if anything it makes review more important because the code looks finished sooner.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Does using an AI assistant inside FileMaker (like Claude-based tools) change who owns the bugs?\u003C\u002Fstrong>\nNo. Whoever accepts and deploys the code owns the responsibility for it, regardless of who or what wrote it. "The AI wrote it" is not a valid answer to "why did this feature corrupt three records."\u003C\u002Fp>\n\u003Cp>\u003Cstrong>How do you know if an AI-generated feature will still work after a model or platform update?\u003C\u002Fstrong>\nYou generally don't know for certain — which is why features with a live AI dependency need monitoring and a fallback plan, not just an initial test pass.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Is it faster overall to use AI-generated features despite the extra review?\u003C\u002Fstrong>\nOften yes, especially for first drafts, repetitive scripting, or generating layout scaffolding — but only if the review step is actually built into your process rather than skipped under deadline pressure.\u003C\u002Fp>\n\u003Cp>Evaluating an AI-generated feature properly takes real discipline, and it's exactly the kind of judgment call that benefits from a second set of experienced eyes. Loggix helps teams build that discipline into their FileMaker systems and connected ERP or API workflows — whether that means reviewing and hardening an AI-generated script before it goes live, building the audit trails and permission structures a higher-risk feature needs, or simply sitting down together to map out which AI-assisted shortcuts are actually safe for your business and which ones need a human in the loop.\u003C\u002Fp>\n","Jeroen","2026-07-24",1784901673000,[19,20,21,22,23,24,25],"AI-generated code","FileMaker development","AI in business software","code review","FmBetterForms","quality assurance","custom software","\u002Fapi\u002Fknowledge\u002Fimage\u002F320\u002F?v=eed5f9054ab4",false,"",null,{"title":31,"slug":32},"Modern Software Development","modern-software-development",{"title":34,"slug":35},"Necessity-Driven Development: start with what must exist","necessity-driven-development-start-with-what-must-exist"]