Directory / Tags
noul
- 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.
- Autoresearch feature discoveryA loop that proposes new questions, turns free text into numeric features with them, and uses a regressor's mistakes to decide what to ask next.
- Classifying RAG passagesJudge each retrieved passage before it reaches the answering model: keep what helps, flag what contradicts the question, and drop anything carrying a hidden instruction.
- Guardrails for LLMsScreen what goes into and comes out of an LLM app in a single request: describe the hazards, score how bad complying would be, and let your code decide to pass, review, block or reroute.
- Knowledge graph entity alignmentDecide which candidate pairs from two product catalogues are the same thing. A single Score question with three levels maps straight onto the three actions: merge, leave apart, or send to a curator.
- 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.
- Re-rankingTake a keyword-search shortlist and ask one question per query and candidate pair to reorder it. Worked through on legal case retrieval, where it lifts the right passage towards the top.
- SDE cascadeA two-stage cascade for structured data extraction: a small model extracts, a verification step checks, and only the doubtful cases go to a large reasoning model. Most of the quality for a fraction of the cost.
- Self-consistency: noulsSend borderline probabilities to a human reviewer instead of forcing a yes or no, while keeping the underlying values in view so the routing stays explainable.
- 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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