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.

The author argues that you don't need massive AI models for most real-world applications—smaller models handle instruction-following well and fail predictably when they don't know something, rather than confidently making things up like larger models do. He's building a system that lets you swap between different AI providers through a single interface.

• Small models hallucinate less reliably than large ones; when told clearly that information is missing, they admit it instead of inventing plausible-sounding answers
• Larger models produce more polished fabrications, making their hallucinations harder to catch in production
• The author's architecture (Pepper) uses a provider-neutral layer that treats adding new AI backends as simple routing and translation work, not a full rewrite