Large language models aren't a novelty for appraisers — they're a new interface into the same evidence appraisers already trust. Here's how we use them inside Comp Chirp, and where we deliberately don't.
What LLMs are good at
- Translating an appraiser's plain-English request into a structured comp query.
- Normalizing messy listing text into make, model, year, and configuration fields.
- Surfacing the reasoning behind a ranked comp set for the workfile narrative.
What we don't ask them to do
We do not use LLMs to infer values. Every number Comp Chirp surfaces is traceable to a listing, an auction result, or a historical sale — not to a model's opinion of what a value should be.
"An LLM is a great research assistant and a terrible appraiser. The trick is knowing which seat to put it in."
The advanced query pattern
Under the hood, an advanced query is a structured plan the LLM writes and then executes against curated data. The plan is inspectable, the results are cited, and every step is logged. That's how a natural-language question becomes a defensible comp set.
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Pull defensible comps, keep the reasoning tight, and ship the workfile faster.
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