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# AI call inspector (Admin → AI calls)
**Admin → AI calls** (`/admin/ai-calls`, platform admin only) shows the exact
system prompt, user prompt and raw response of every LLM call, per tenant and per
user.
Before this existed the prompt was only ever in the worker log, truncated to 500
runes from each end, and `processed_products.gpt_response` kept response metadata
without the prompt at all — so "what exactly did we send for this job" had no
answer.
## What is captured
Everything resolved through `aiprovider.Service`, tagged by role:
| role | where it comes from |
|---|---|
| `processing` | product enhance (title / description / meta / attrs) |
| `categorize` | taxonomy pick when the product has no category |
| `seo_meta` | standalone SEO meta generation |
| `campaign` | marketing email generation |
| `support` | support draft assist |
Each row carries company, the user who started the work, job id, raw product id,
model, provider mode, finish reason, token counts, duration, and the provider error
when the call failed.
## How it works
- `internal/aiaudit` is a leaf package: context helpers plus the writer. Callers
attach detail with `aiaudit.WithCall(ctx, …)`; `ProcessJob` sets company/user/job
once and `processOne` narrows it to the product.
- `aiprovider` wraps every Completer it hands out (`audit.go`). One capture point,
so a new caller cannot forget to log. The wrapper forwards
`CompleterWithOptions` when the client has it — otherwise every enhance call
would silently lose `MaxTokens` / `Temperature` / `ReasoningEffort`.
- Capture never breaks the call it observes: write failures are swallowed after one
log line, and the insert uses `context.WithoutCancel` so a cancelled job still
records the call that was in flight.
## Retention
Rows are large and high volume — one per product per language, a few KB each. They
are a **debugging buffer, not an audit trail**:
- `aiaudit.RetentionDays` = 7.
- The worker prunes on its retention tick (`aiaudit.CleanupExpired`).
- Each body is capped at 60k runes.
- Never build billing or reporting on this table.
For scale: a 25k-product job writes roughly 50150 MB, which ages out within a
week.
## API
Platform admin only (`RequirePlatformAdmin`; 401 unauthenticated, 403 non-admin).
```
GET /api/admin/ai-calls?company_id=&user_id=&job_id=&raw_product_id=&role=&outcome=&q=&limit=&before=
GET /api/admin/ai-calls/{id}
```
The list returns lengths plus 240-rune previews — one unfiltered request must never
stream a tenant's whole prompt corpus. Full bodies come from the detail endpoint,
one row at a time. `q` searches system prompt, user prompt and response.
`before` is the RFC3339 cursor returned as `next_before`.
## Reading a row
Start from the outcome, then the prompt:
- `outcome=failed` — the `error` field holds the provider error. If it is a dial or
5xx error the model never answered and enhance fell back to supplier copy.
- `role=processing` with a short `user_length` — the category formula probably did
not key. Check that the prompt contains `GPT predloga:` / `Product template:` and
a `<name>{` block.
- Response present but the product still looks like the feed — compare the response
against the formula sections; a reply that ignores them is rejected by the
formula gate and marked `synthesized`/`refused` (see docs/category-formulas.md).
A job with no rows at all made no LLM calls: it was either hash-skipped
(`ai_enhance outcome=skip reason=unchanged_hash`), gated by plan/credits, or the
provider was unset.
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stored, so a reprocess tries again rather than caching the bad copy.
`processing: ai_enhance outcome=… category_uid=… title_formula=… desc_formula=…
formula_override=…` in the worker log tells you which of these happened.
formula_override=…` in the worker log tells you which of these happened — and
**Admin → AI calls** shows the exact prompt and reply for the call itself
(docs/ai-call-inspector.md).