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Introducing Clairic, a new System One class of extraction models
Cloudflare introduced Clairic, an extraction-focused 'System One' model that avoids hallucinations by performing single-pass data processing without generative output.
What: Clairic, available on Workers AI, is an extraction model designed to identify, compute, and forecast values from unstructured data. Unlike traditional LLMs, it outputs typed data (numbers, dates, spans) rather than generating text tokens, effectively eliminating hallucination risks in structured data tasks.
Why it matters: By separating 'System One' extraction and decision tasks from 'System Two' generative reasoning, developers can build more reliable and cost-effective pipelines that don't rely on expensive, error-prone LLM generation for data parsing.
Takeaway: Try Clairic on Workers AI (@cf/cloudflare/clairic) to replace generative extraction pipelines for invoice processing or structured data extraction.
Deep dive
- System One vs System Two: Categorizes AI based on thinking speed: System One is fast/parallel (extraction/decisions); System Two is slow/deliberate (reasoning/generative).
- Typed Extraction: Clairic returns specific formats like numbers, dates, and lists with character-level source offsets.
- No Generative Tokens: Since it is a regression/extraction model, it cannot produce prose, preventing text hallucination.
- Confidence Scoring: Each output comes with an associated confidence metric, allowing developers to set programmatic thresholds for manual intervention.
- Performance Gains: Cascading extraction/decision models significantly reduces latency and cost compared to end-to-end LLM processing.
Decoder
- System One model: Non-generative, fast-inference model architecture designed for classification, extraction, or regression tasks performed in a single forward pass.
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