## Problem
Users keep correcting the same transactions over and over. The AI
mislabels a merchant (e.g. supermarket → fuel), the user fixes it, and
the next near-identical transaction from that merchant gets mislabeled
the same way again. Today a correction is logged and the offending ai
rule is self-healed, but the system only *forgets* its mistake — it
never *remembers* the user's fix.
## Approach
A correction now becomes a deterministic, forward-looking
`AutomationRule` (new `RuleOrigin::Correction`). The next matching
transaction is categorized by that rule **before the model ever runs**
(`ApplyAutomationRules` is synchronous and runs ahead of AI
categorization), ending the loop. Zero model cost, instant, reuses the
existing rule engine.
**Matching key** (in order):
1. **Merchant** (`creditor_name`/`debtor_name`, exact `==`) when present
— stable even as the description varies.
2. Otherwise the **description's distinctive tokens** (`in` /
AND-of-`in`), extracted by the shared `DescriptionTokenizer` (noise
tokens dropped by document frequency, language-agnostic), **guarded**
against over-broad rules that could silently mis-file en masse. If
guarded out → nothing is learned, silently.
## Deliberate decisions (from a design walkthrough)
- **Forward-only**: never retroactively re-categorizes existing
transactions.
- **Learn only from system categorizations** (AI label, ai rule, or a
prior correction rule) — never from one-off manual filing, bank
categories, or the user's own hand-authored rules.
- **A key lives in exactly one correction rule**, so changing your mind
moves it to the new category. Correcting a transaction a prior
correction rule categorized is also learnable, so correction rules stay
fixable in-flow.
- Correcting to *uncategorized* learns nothing but still self-heals the
ai rule.
- **Safety net**: correction rules are visible/editable in
`settings/automation-rules` (marked with the AI sparkle, tooltip
"Learned from your correction"); the transactions table shows a toast
with an instant **Undo**.
## Review pass (two independent agents + live QA)
- **HIGH fix** (`67fc4293`): an ai rule could out-rank a freshly learned
correction and re-apply the wrong category (when the corrected
transaction was a *direct* model label with no rule id). Now every ai
rule holding the merchant is swept on correction. Regression test added.
- **Refactor** (`ca743fde`): collapsed duplicated clause-append logic;
removed a speculative unused enum helper.
- **Coverage** (`12ceb0f7`): debtor_name path, single-token description
clause, encrypted-description fail-safe.
- **Toast conflict fix** (`382c8169`, found in live QA): correcting an
AI transaction fired both the new "Learned …" toast and the pre-existing
"Transaction categorized → Automatize" prompt, which invited the user to
manually create the rule the correction had just created. Made them
mutually exclusive. Adds a browser test for the inline-correction flow.
- **Settings icon** (`f3f882b6`): correction rules now show the AI
sparkle in settings, like ai rules.
## Verified end-to-end (against a running instance)
Drove the real UI with a browser: correcting an AI-mislabeled
transaction creates the correction rule, shows the "Learned · Undo"
toast (no competing Automatize prompt), and Undo deletes the rule while
keeping the correction. Confirmed for **merchant** keys and the
**description-only** path — including that a later "practically
identical" description (different surrounding text, no merchant) is
caught by the rule, while a near-miss sharing only one distinctive token
is correctly **not** caught.
## Open question for reviewers
**`debtor_name` (P2P) as a rule key.** For incoming transfers the
merchant key falls back to the sender's name, so correcting one can
create a rule keyed on a person's name (useful for recurring transfers
from a roommate, but a possible privacy surprise; the name appears in
the rule title in settings). This matches the existing tier-2 learner's
behaviour. Keep as-is, or restrict correction rules to `creditor_name`
only? Happy to change.
## Testing
- `tests/Feature/Ai/CategoryOverrideHandlerTest.php`: merchant +
description learning, next-transaction match, over-broad rejection,
change-of-mind move, correct-to-null self-heal, the HIGH regression,
debtor_name, single-token, encrypted fail-safe.
- `tests/Browser/CategoryCorrectionLearningTest.php`: inline correction
→ toast → learned rule → undo.
- `automation-rule-title.test.tsx`: the AI sparkle shows for `ai` and
`correction`, not `user`.
- Full AI suite green (106 tests); transaction/bulk-update suites green
(62). Pint + Prettier + ESLint clean.
No new dependency, no migration (the `origin` column is a free-text
string). The feature is implicitly gated by AI categorization — with no
AI categorization there is nothing to correct and nothing is learned.
Suggests transaction categorization rules during onboarding.
After a sync or import, it groups the uncategorized transactions, asks
Gemini (via laravel/ai) to map the common merchants to categories, and
shows the results for review. The user edits or drops any and creates
the ones they want. During onboarding the accepted rules also categorize
existing transactions right away.
Off by default: it needs the `AiRuleSuggestions` Pennant flag and a
per-user AI consent. The model and thresholds are config-driven.
`ai:suggest-rules {user}` prints what a user would get.
The settings-page surface and monthly regeneration are a follow-up.
Víctor Falcón