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choice
- pg-jevA PostgreSQL extension that lets you write a WHERE clause in plain language: jev(people, the name is European) is an ordinary boolean function, so it joins, groups and sorts like any other. Each row is judged by Jev, batched many rows per request and cached per row, with jev_prob, jev_choice and jev_score for ranking and classifying. No embeddings, no vector column.
- Jev UltrafastA browser agent from Browser Use where Jev decides every step. From a numbered table of the page's elements it picks the operation and the target in a single request, and a small LLM is called only when text has to be typed. Its demo runs a Google Flights search from one plain-English goal in about seven seconds.
- Classification using confidenceSort company annual reports into fine-grained industry groups with one Choice each, and use the answer's own confidence to fall back to the broader division when the model is unsure.
- Date extractionPull absolute and relative dates out of documents by asking for the parts that are named, then resolving and validating them in code, with low-confidence cases sent for review.
- Double-checking citationsCheck whether a quoted source really backs the claim it is attached to. One question gives the verdict, and its confidence decides which citations a person should look at.
- Function callingTurn a plain-English request into a call to an ordinary typed function. Each function name and each closed-set argument becomes a question, so the answer is always something your code can dispatch.
- Hierarchical classificationClassify documents into deep category trees, such as patents, retail products, biomedical topics and source code, by running a beam search over the probabilities of each level.
- Line-by-line searchSemantic search over a long terms-of-service document in one request: a Choice picks the line that answers a plain-language query, and a Noul checks whether the document answers it at all.
- Parallel questionsAsk a whole briefing's worth of questions about one long document in a single call instead of one call each. Same answers, at a fraction of the cost and time.
- Pre-parsed value extractionLet a regex find every candidate email, phone number or amount, then have the model select the right one. The output is always a verbatim value from the text: select, do not generate.
- Self-consistency: choicesAdd an explicit uncertain outcome to moderation decisions, then weigh how often labels agree against how much gets actioned automatically.
- Skill suggestionPick at most one skill for an agent's turn from a catalogue of well over a hundred. One request ranks them all and asks whether any is needed; a second reads the top few closely and may reject them all.
- Structure recoveryRebuild Markdown from plain text that lost its formatting. One request rejoins hard-wrapped lines; a second labels each block as heading, list, code or callout.
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