Two sources of hallucinated values in receipt parsing:
1. The LLM extraction prompt had no explicit "don't guess" constraint, so
when Tesseract produced garbled OCR text the LLM substituted plausible-
looking values (wrong vendor names, wrong totals) instead of returning
safe defaults.
2. The date field asked the LLM to extract the date from the OCR text even
when date_hint (from the filename timestamp, e.g. 20260509_180857.jpg)
was already available — a reliable signal that was being ignored.
expenses_agent._parse_receipt_text:
- LLM path: new prompt leads with "copy values EXACTLY, do NOT guess or
infer"; adds "if OCR looks corrupted, return safe default rather than
a more logical value"; injects date_hint directly as an authoritative
value when available so the LLM never needs to extract the date.
- Vision fast path: normalise "null" string for date the same way as time;
prefer date_hint over a null date returned by the vision model.
receipt_parser._ocr_image_vision:
- Vision prompt now leads with the same "copy exactly, do not guess"
constraint and explicitly accepts null for date/time when not clearly
visible, matching the conservative tone of the LLM extraction prompt.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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