{"id":"16-verify","number":16,"slug":"verification-and-guardrails","title":"Verify and guard","short":"Verify and guard","module":"Composing judgments","summary":"Decision models are cheap and fast enough to check everything: every citation an LLM writes, every message going in and out, every extracted field. Here is a citation check, a one-request guardrail screen, why two independent checks beat one, and why none of this is a security boundary.","voice":"Jessica","scenes":[{"template":"title","chapter":"Intro","narration":"Language models write. Decision models can check what they wrote, fast and cheap enough to check every single output. This lesson is about judges and guards.","params":{"kicker":"Lesson 16","module":"Module 4 · Composing judgments","title":"Verify and *guard*","subtitle":"Fast judges for everything an LLM writes, reads, or does.","cast":["snap",{"who":"mull","mood":"think"}]},"start":0.5,"dur":11.806,"words":[{"text":"Language","s":0.8,"e":1.28},{"text":"models","s":1.36,"e":1.68},{"text":"write.","s":1.84,"e":2.48},{"text":"Decision","s":2.72,"e":3.12},{"text":"models","s":3.2,"e":3.52},{"text":"can","s":3.6,"e":3.76},{"text":"check","s":3.88,"e":4},{"text":"what","s":4.08,"e":4.16},{"text":"they","s":4.2,"e":4.24},{"text":"wrote,","s":4.32,"e":4.96},{"text":"fast","s":5.04,"e":5.44},{"text":"and","s":5.56,"e":5.68},{"text":"cheap","s":5.8,"e":5.92},{"text":"enough","s":6,"e":6.24},{"text":"to","s":6.32,"e":6.4},{"text":"check","s":6.56,"e":6.72},{"text":"every","s":6.8,"e":7.12},{"text":"single","s":7.28,"e":7.6},{"text":"output.","s":7.68,"e":8.64},{"text":"This","s":8.76,"e":8.88},{"text":"lesson","s":9.08,"e":9.28},{"text":"is","s":9.36,"e":9.44},{"text":"about","s":9.52,"e":9.68},{"text":"judges","s":9.84,"e":10.32},{"text":"and","s":10.4,"e":10.48},{"text":"guards.","s":10.56,"e":11.44}]},{"template":"request","chapter":"Check the citation","narration":"Say an LLM writes: walking seven thousand steps a day cuts hospitalization risk by thirty one percent, the study proved. And it cites a quote. One Choice checks the claim against the quote in the context of the source. The answer: partially, at point nine nine. The number is right, but the source is an observational study that proved nothing.","params":{"model":"jev-1.13.0","latency":0.24,"state":{"claim":"Walking 7,000 steps a day cuts hospitalization risk by 31%, the study proved.","quote":"Participants who walked at least 7,000 steps a day had a 31% lower risk of hospitalization…","source":"…The authors caution that the study was observational."},"questions":[{"id":"s","type":"choice","text":"Does `quote`, in the context of `source`, support `claim`? (supports / partially / contradicts / unrelated)","ans":{"choice":"partially","p":0.99},"at":"One Choice"}],"send_at":"One Choice","ans_at":"The answer","footer":"Real answer, jev-1.13.0. “partially” = backs part of the claim but overstates it."},"start":12.306,"dur":23.781,"words":[{"text":"Say","s":12.606,"e":12.926},{"text":"an","s":13.046,"e":13.166},{"text":"LLM","s":13.246,"e":13.886},{"text":"writes:","s":13.966,"e":14.686},{"text":"walking","s":14.926,"e":15.326},{"text":"seven","s":15.406,"e":15.646},{"text":"thousand","s":15.806,"e":16.046},{"text":"steps","s":16.126,"e":16.366},{"text":"a","s":16.446,"e":16.526},{"text":"day","s":16.606,"e":16.846},{"text":"cuts","s":17.006,"e":17.246},{"text":"hospitalization","s":17.486,"e":18.126},{"text":"risk","s":18.206,"e":18.526},{"text":"by","s":18.646,"e":18.766},{"text":"thirty","s":18.846,"e":19.086},{"text":"one","s":19.166,"e":19.246},{"text":"percent,","s":19.326,"e":20.046},{"text":"the","s":20.086,"e":20.126},{"text":"study","s":20.206,"e":20.446},{"text":"proved.","s":20.526,"e":21.246},{"text":"And","s":21.406,"e":21.566},{"text":"it","s":21.646,"e":21.726},{"text":"cites","s":21.766,"e":21.966},{"text":"a","s":22.006,"e":22.046},{"text":"quote.","s":22.126,"e":23.006},{"text":"One","s":23.086,"e":23.166},{"text":"Choice","s":23.246,"e":23.566},{"text":"checks","s":23.646,"e":23.886},{"text":"the","s":23.966,"e":24.046},{"text":"claim","s":24.206,"e":24.366},{"text":"against","s":24.566,"e":24.766},{"text":"the","s":24.846,"e":24.926},{"text":"quote","s":25.086,"e":25.246},{"text":"in","s":25.286,"e":25.326},{"text":"the","s":25.406,"e":25.486},{"text":"context","s":25.726,"e":25.966},{"text":"of","s":26.006,"e":26.046},{"text":"the","s":26.126,"e":26.206},{"text":"source.","s":26.286,"e":27.166},{"text":"The","s":27.246,"e":27.326},{"text":"answer:","s":27.406,"e":28.126},{"text":"partially,","s":28.286,"e":29.006},{"text":"at","s":29.126,"e":29.246},{"text":"point","s":29.366,"e":29.486},{"text":"nine","s":29.566,"e":29.726},{"text":"nine.","s":29.806,"e":30.526},{"text":"The","s":30.606,"e":30.686},{"text":"number","s":30.846,"e":31.006},{"text":"is","s":31.086,"e":31.166},{"text":"right,","s":31.246,"e":31.726},{"text":"but","s":31.806,"e":32.046},{"text":"the","s":32.126,"e":32.206},{"text":"source","s":32.366,"e":32.526},{"text":"is","s":32.606,"e":32.686},{"text":"an","s":32.766,"e":32.846},{"text":"observational","s":32.926,"e":33.566},{"text":"study","s":33.646,"e":34.046},{"text":"that","s":34.166,"e":34.286},{"text":"proved","s":34.486,"e":34.686},{"text":"nothing.","s":34.766,"e":35.486}]},{"template":"code","chapter":"Check the citation","narration":"But notice what we didn't ask the model. Is the quote in the source word for word? We tried, and got point eight six, for a quote that is there exactly. That's a string match, and code gets it right every time. Let the model judge meaning. 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Unsiloed reads a medication list and scores each field, then asks Jev whether each dose is plausible. A coffee stain over one dose fails the reading check, while Jev, seeing only the text, calls it plausible. Ten ibuprofen tablets three times a day reads perfectly, but Jev puts its plausibility at point oh four. Each check flags a review need the other misses.","params":{"eyebrow":"Unsiloed + Jev, document review","text":"Each check flags a review need *the other misses*.","sub":"Reading confidence catches the coffee stain. 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TypeSafe's notes say the state is data, but Jev doesn't treat it as hostile by default, and text written to steer it can move the answer. Our injection test was caught, but don't build on that. If an agent can read private data, see untrusted content, and send data out, Simon Willison's lethal trifecta, no classifier makes it safe. Remove one of the three.","params":{"eyebrow":"Simon Willison, the lethal trifecta","text":"Private data + untrusted content + a way out. *A guard reduces risk. It doesn't remove it.*","sub":"If an agent has all three, take one capability away.","sub_at":"Remove one","art":"lock","tone":"bad","punch":"no classifier makes it safe"},"start":142.609,"dur":25.871,"words":[{"text":"Now","s":142.909,"e":143.149},{"text":"the","s":143.229,"e":143.309},{"text":"warning.","s":143.389,"e":143.869},{"text":"TypeSafe's","s":144.269,"e":144.829},{"text":"notes","s":144.909,"e":145.229},{"text":"say","s":145.309,"e":145.469},{"text":"the","s":145.509,"e":145.549},{"text":"state","s":145.629,"e":145.869},{"text":"is","s":145.949,"e":146.109},{"text":"data,","s":146.189,"e":146.909},{"text":"but","s":146.989,"e":147.229},{"text":"Jev","s":147.309,"e":147.549},{"text":"doesn't","s":147.629,"e":147.869},{"text":"treat","s":147.909,"e":148.109},{"text":"it","s":148.189,"e":148.269},{"text":"as","s":148.349,"e":148.429},{"text":"hostile","s":148.589,"e":148.909},{"text":"by","s":148.989,"e":149.069},{"text":"default,","s":149.149,"e":149.949},{"text":"and","s":150.029,"e":150.109},{"text":"text","s":150.189,"e":150.509},{"text":"written","s":150.629,"e":150.749},{"text":"to","s":150.829,"e":150.909},{"text":"steer","s":150.989,"e":151.229},{"text":"it","s":151.309,"e":151.469},{"text":"can","s":151.549,"e":151.629},{"text":"move","s":151.709,"e":151.949},{"text":"the","s":151.989,"e":152.029},{"text":"answer.","s":152.109,"e":152.589},{"text":"Our","s":153.069,"e":153.149},{"text":"injection","s":153.229,"e":153.549},{"text":"test","s":153.629,"e":153.869},{"text":"was","s":153.949,"e":154.029},{"text":"caught,","s":154.109,"e":154.589},{"text":"but","s":154.749,"e":154.909},{"text":"don't","s":154.989,"e":155.229},{"text":"build","s":155.309,"e":155.469},{"text":"on","s":155.549,"e":155.629},{"text":"that.","s":155.709,"e":156.149},{"text":"If","s":156.349,"e":156.509},{"text":"an","s":156.549,"e":156.589},{"text":"agent","s":156.669,"e":156.989},{"text":"can","s":157.069,"e":157.149},{"text":"read","s":157.229,"e":157.469},{"text":"private","s":157.629,"e":157.789},{"text":"data,","s":157.869,"e":158.429},{"text":"see","s":158.549,"e":158.669},{"text":"untrusted","s":158.749,"e":159.229},{"text":"content,","s":159.309,"e":159.949},{"text":"and","s":160.069,"e":160.189},{"text":"send","s":160.269,"e":160.509},{"text":"data","s":160.669,"e":160.829},{"text":"out,","s":160.909,"e":161.549},{"text":"Simon","s":161.629,"e":161.869},{"text":"Willison's","s":161.949,"e":162.349},{"text":"lethal","s":162.509,"e":162.829},{"text":"trifecta,","s":162.909,"e":163.949},{"text":"no","s":164.109,"e":164.269},{"text":"classifier","s":164.349,"e":164.989},{"text":"makes","s":165.109,"e":165.229},{"text":"it","s":165.309,"e":165.389},{"text":"safe.","s":165.589,"e":166.189},{"text":"Remove","s":166.429,"e":166.669},{"text":"one","s":166.829,"e":166.909},{"text":"of","s":166.989,"e":167.069},{"text":"the","s":167.149,"e":167.229},{"text":"three.","s":167.389,"e":167.869}]},{"template":"quiz","chapter":"Quick check","narration":"Quick check. You want to know whether an LLM's answer quotes the source document exactly. What's the best check?","params":{"q":"Does the LLM's quote appear in the source **word for word**? Best check?","options":["A Noul: “Is the quote in the source verbatim?”","A Score for similarity","A string match in code","Ask the LLM to double-check itself"],"answer":2,"explain":"Exact text checks are code's job. Use the decision model for meaning: does the quote, in context, support the claim?"},"start":168.48,"dur":13.049,"words":[{"text":"Quick","s":168.78,"e":169.02},{"text":"check.","s":169.1,"e":169.62},{"text":"You","s":169.82,"e":169.98},{"text":"want","s":170.06,"e":170.14},{"text":"to","s":170.18,"e":170.22},{"text":"know","s":170.3,"e":170.46},{"text":"whether","s":170.54,"e":170.78},{"text":"an","s":170.9,"e":171.02},{"text":"LLM's","s":171.1,"e":171.66},{"text":"answer","s":171.9,"e":172.14},{"text":"quotes","s":172.22,"e":172.54},{"text":"the","s":172.62,"e":172.7},{"text":"source","s":172.78,"e":173.02},{"text":"document","s":173.1,"e":173.5},{"text":"exactly.","s":173.66,"e":174.94},{"text":"What's","s":175.02,"e":175.18},{"text":"the","s":175.22,"e":175.26},{"text":"best","s":175.42,"e":175.66},{"text":"check?","s":175.74,"e":176.38}]},{"template":"exercise","chapter":"Try it","narration":"Try it. Have your agent add one judge and one guard to your LLM feature.","params":{"title":"Add a judge and a guard","intro":"Paste this into your coding agent:","prompt":"For our main LLM feature, add:\n1. An input guard: one decision-model request with narrow hazard Nouls\n   (injection, PII, abuse, out of scope) + a severity Score, mapped in\n   code to pass / review / block / route (e.g. redact PII).\n2. An output judge: for each claim with a citation, check the quote\n   exists verbatim in code, then ask a Choice whether the quote, in\n   context, supports / partially supports / contradicts the claim.\nLog every verdict with its probabilities, and test on 30 examples."},"start":181.529,"dur":6.018,"words":[{"text":"Try","s":181.829,"e":182.069},{"text":"it.","s":182.149,"e":182.389},{"text":"Have","s":182.709,"e":182.869},{"text":"your","s":182.949,"e":183.029},{"text":"agent","s":183.109,"e":183.429},{"text":"add","s":183.509,"e":183.669},{"text":"one","s":183.829,"e":183.989},{"text":"judge","s":184.069,"e":184.389},{"text":"and","s":184.469,"e":184.629},{"text":"one","s":184.789,"e":184.869},{"text":"guard","s":185.029,"e":185.349},{"text":"to","s":185.389,"e":185.429},{"text":"your","s":185.509,"e":185.589},{"text":"LLM","s":185.669,"e":186.149},{"text":"feature.","s":186.389,"e":186.949}]},{"template":"recap","chapter":"Recap","narration":"So: decision models make cheap, fast judges. Check meaning with the model and text with code. Screen messages with one request and map the answers to actions. Use independent checks. And remember, a guard is not a security boundary. That's module four. Next: decision models inside agents.","params":{"items":["Judge every output: claims vs sources, in one fast call.","Screen messages: hazards + severity → pass, review, block, route.","Model for meaning, code for exact text.","Guards reduce risk; with the lethal trifecta, remove a capability."],"next":"Decision models inside agents","next_at":"Next"},"start":187.547,"dur":23.216,"words":[{"text":"So:","s":187.847,"e":188.367},{"text":"decision","s":188.567,"e":188.967},{"text":"models","s":189.047,"e":189.367},{"text":"make","s":189.447,"e":189.607},{"text":"cheap,","s":189.767,"e":190.247},{"text":"fast","s":190.327,"e":190.647},{"text":"judges.","s":190.807,"e":191.567},{"text":"Check","s":191.767,"e":192.007},{"text":"meaning","s":192.167,"e":192.327},{"text":"with","s":192.407,"e":192.487},{"text":"the","s":192.527,"e":192.567},{"text":"model","s":192.647,"e":193.047},{"text":"and","s":193.127,"e":193.207},{"text":"text","s":193.367,"e":193.687},{"text":"with","s":193.767,"e":193.847},{"text":"code.","s":193.927,"e":194.327},{"text":"Screen","s":194.647,"e":194.967},{"text":"messages","s":195.047,"e":195.447},{"text":"with","s":195.527,"e":195.687},{"text":"one","s":195.767,"e":195.927},{"text":"request","s":196.207,"e":196.487},{"text":"and","s":196.647,"e":196.727},{"text":"map","s":196.807,"e":197.047},{"text":"the","s":197.087,"e":197.127},{"text":"answers","s":197.287,"e":197.607},{"text":"to","s":197.687,"e":197.767},{"text":"actions.","s":197.847,"e":198.687},{"text":"Use","s":198.887,"e":199.127},{"text":"independent","s":199.287,"e":199.767},{"text":"checks.","s":199.847,"e":200.407},{"text":"And","s":200.647,"e":200.727},{"text":"remember,","s":200.807,"e":201.527},{"text":"a","s":201.567,"e":201.607},{"text":"guard","s":201.767,"e":202.087},{"text":"is","s":202.247,"e":202.407},{"text":"not","s":202.567,"e":202.887},{"text":"a","s":202.967,"e":203.007},{"text":"security","s":203.047,"e":203.447},{"text":"boundary.","s":203.527,"e":204.287},{"text":"That's","s":204.567,"e":204.807},{"text":"module","s":204.887,"e":205.127},{"text":"four.","s":205.287,"e":205.887},{"text":"Next:","s":206.167,"e":206.887},{"text":"decision","s":207.087,"e":207.287},{"text":"models","s":207.367,"e":207.767},{"text":"inside","s":207.847,"e":208.247},{"text":"agents.","s":208.327,"e":209.207}]}],"total":211.163,"audio":"/audio/16-verify.mp3"}