1 link tagged with all of: jev + 10 k extraction + decision layer
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TypeSafe's Jev model works best as a decision layer for SEC filing extraction—you pre-generate candidate values with regex or a small model, then Jev picks the right one and returns confidence scores. This setup costs $0.042 per million input tokens, runs in 70–500 ms, and eliminates the expensive retry loops of pure generative approaches.
- Never ask Jev to invent values; feed it candidates first, then use Choice/Noul/Score primitives to select, verify presence, and score confidence—this replaces unreliable post-processing and validation cycles.
- A production pipeline chunks the 10-K, runs a cheap extractor (regex or small LLM), batches Jev questions in parallel, then gates on confidence thresholds: ≥0.90 auto-stores, 0.70–0.90 routes to heavier review, <0.70 goes to humans.
- At $0.042 per million tokens with free output and sub-500 ms latency, a 50-page 10-K costs a few cents and processes in seconds—a practical advantage during the seasonal filing flood after fiscal year-end deadlines.