Lead Scoring with a Judgment-Only Model

#The scenario

Inbound leads arrive from forms, trials, webinars, and lists — hundreds or thousands a week. Most will never buy. Sales time is finite, and personalized outreach written by an LLM still costs money per lead. The job: decide who deserves effort before spending it.

#Why Jev fits

Lead qualification is a scoring problem with structured input — exactly the judgment model's home turf. Firmographic and behavioral fields go in; a fit verdict comes out. Because the per-judgment cost is low, you can re-score the entire pipeline continuously (new signals, changed behavior) instead of scoring once at intake and letting the list go stale.

#Judgment design example

Structured input per lead:

Judgments to define:

1. Binary: "Matches our ideal customer profile?" → instant yes/no gate.
2. Choice: "Which tier?" → A (sales call now) / B (nurture sequence) / C (self-serve only) / D (disqualify).
3. Score: "Buying-readiness 0–100" → ranks the A-tier queue and re-ranks daily as signals accumulate.

Design note: keep ICP criteria explicit in the input fields rather than hoping the model infers them. A judgment is only as sharp as the question.

#Where the LLM sits

Only on leads that pass. Tier A gets LLM-drafted personalized outreach referencing the actual signals; tier B gets generated nurture content; tiers C and D consume zero generation budget. The front-filter pipeline applies directly — the "filter rate" here is the fraction of leads sales actually touches, and it should be small.

A second LLM slot: when a rep disputes a score, an LLM can write the explanation of the lead record for the CRM note — generation where a human actually reads it.

#Watch-outs

• Garbage fields, garbage verdicts. If industry is free-text junk, normalize it before judging.
• Score ≠ truth. Calibrate thresholds against closed-won history before automating routes.
• Bias check. Audit tier assignments by segment; a cheap filter that systematically misroutes a segment is a revenue leak, not a saving.

#Sources

• "Jev is HERE. How to use it" — Greg Isenberg (~293K views, 1 day).
• "Jev + GPT-6 Astra = 400X Cheaper" — Jack Roberts (~21K views, 11 hours) — the cost math behind filtering before generation.
• "Jev explained in 7min" — Caleb Writes Code — the judgment-paradigm overview.

Unofficial fan-made handbook. Not affiliated with TypeSafe AI or jev.com.