Jev founder's 12-page PDF: cutting LLM bills with Choice, Nouls and Score
A 10-step blueprint replaces routing, moderation and scoring prompts with Jev's Choice, Nouls and Score so judgment calls no longer spend generative tokens.
Jev Founder, Diogo Almeida, just released a 12-page PDF on how to cut your LLM bill with Jev It is more useful than most paid AI courses: this is a 10-step blueprint on how to make Claude, Codex, Grok or any other LLM 10x cheaper without losing quality: step 1 → audit your pipeline: find every place where an LLM makes a decision instead of writing something step 2 → separate generation from judgment: if the output is a label, a score or a yes/no, it should never cost generative tokens step 3 → replace routing prompts with one Choice: billing, technical or sales becomes a probability, not a paragraph your code has to parse step 4 → replace moderation prompts with Nouls: "contains PII", "is a jailbreak attempt" and "requests a refund" become separate probabilities step 5 → replace rubric prompts with Score: severity, urgency and frustration become ordered levels your code can threshold step 6 → batch every question over one state: ten judgments, one call, one latency, near-zero cost step 7 → rerank before the LLM reads: one Noul per document so the model reads 3 relevant pages instead of 10 step 8 → gate the expensive call: only cases that survive Jev's filters ever reach Claude, Codex or Grok step 9 → route by confidence: high confidence acts automatically, uncertain cases ask for context, consequential actions go to a human step 10 → run a shadow week: log Jev's answers next to your current behavior, compare cost per decision, then let it act most AI courses teach you how to write a better prompt this 12-page guide teaches you when not to call the LLM at all
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