{"node":{"id":"3756718a-ef21-427f-ad5f-b4f9bfebc398","handle":"3756","type":"note","authorName":"Vincent Yeung","intent":"recipe","title":"How to capture what a person actually said: the three-context pattern with a decision model","aiSummary":"How to get a record of what someone actually said in a conversation you took part in, without your own reading of it creeping in. Five steps across three separate AI sessions: extract the raw transcript cut into threads, pull out the person's own words tagged by who said them, score each claim against a one-sentence purpose with a decision model, write only from the scored claims, then check the output against a list. With an example label set and when not to use it.","content":"**1. Extract the transcript.**\n\n- Keep only the person's text and the AI's text. Reduce each tool call to a one-line marker, and drop tool results.\n- Do not summarise or rewrite. The transcript is the evidence.\n- Find the start by its position, or by the person's own turn. A phrase can be quoted back later in the conversation, and a script that looks for it then starts in the wrong place.\n- Cut the transcript into threads. A thread is one subject the person kept coming back to. A single message is too small to judge on its own, so the thread is what you score.\n- Split a turn that holds an acceptance and something else. \"Okay, drop it. So what counts as done?\" is two claims. Left as one line, the question decides the score and the acceptance is lost.\n\n**2. Pull out the person's own words.** A fresh agent reads only the transcript. A fresh agent is a new AI session with no memory of the conversation. It quotes the person and does not interpret. It pulls out:\n\n- what they claimed\n- what they asked for\n- what they rejected\n- how they framed the subject\n\nIt tags each claim with who said it: the owner, someone relaying for them, or another participant.\n\nText the person pasted from elsewhere sits inside their turns, such as another person's messages or another chatbot's answers. Tell the agent to quote only the person.\n\nA dictated line can come out garbled, or phrased as a question. That is not an unsettled thought. The writer in step 4 turns it into a plain statement using the person's key words.\n\n**3. Score each claim against a purpose.** Write one sentence saying what the output is for. Without it, nothing separates what is relevant from what is not. Then Jev labels each claim against that sentence. Jev is a decision model: it picks one option from a list you give it, and says how sure it is.\n\n- Hand Jev one claim at a time, with the speaker tag written in front of it, and nothing beside it.\n- Score an example together with the claim it illustrates. On its own, an example reads as a fragment and scores as not belonging.\n- Write the labels for each purpose. An example set is below.\n- Jev's label stands. Nobody picks again.\n- You assign the ruling label yourself, from the speaker tag, before Jev runs. A ruling is the owner's own words settling something, in any tone: an instruction, an acceptance, a choice. It sits above every score. Left to Jev, a firm instruction from someone relaying outscores the owner's casual yes.\n- Jev still scores a ruling, but only to see whether it belongs to this purpose.\n- An instruction from someone relaying, or from another participant, is context, however firm.\n\nIf Jev is not available, a fresh agent that sees only the claims and the purpose can pick the labels. The three sessions stay separate, but a language model makes the picks.\n\n**4. Write from the scored claims.** A second fresh agent, which never saw the conversation, writes the output. Give it:\n\n- the scored claims and the person's own framing\n- who the output is for\n- the things the person rejected, which are the strongest signal\n- any position the person agreed to when it was put to them but never said in their own words, labelled as that\n- what the person receiving it has already been told\n\nTell it to leave out jargon, names only your own system uses, and your own interpretations.\n\n**5. Check the output.** Hold it against this list, and have the writer redo any sentence that fails:\n\n- it answers the question first\n- every required section is there\n- every sentence traces back to a claim\n- nothing the person rejected appears\n- no excluded term appears\n- the person's own framing leads\n- no closing line reads as a slogan\n\nTo check clarity, score each sentence on named levels, such as \"a teenager could follow this\", \"one term needs explaining\" and \"the paragraph needs rewriting\". Use levels because a yes or no answer about clarity follows how technical the subject is, whatever the writing.\n\n**The labels.** Write them for each purpose. This set is for the purpose \"what the person designed for how their system should work, in their own words\":\n\n- `belongs`: it states a rule, principle, design choice or definition about how the system should work, even if said casually or with we, they or us.\n- `too_personal`: it is about the person's life, feelings or schedule, or a one-off request, and says nothing about how the system should work.\n- `question_not_claim`: it only asks or wonders, and states no rule, principle or choice.\n\nThe words \"even if said casually\" are in `belongs` because without them, a claim said with \"we\" or \"they\" scores as too personal.\n\n**When not to use it.** Skip it when the person said little about the subject, or has not made up their mind.","capturedAt":"2026-09-19T05:12:10.429Z","createdAt":"2026-09-19T05:12:10.430Z","updatedAt":"2026-10-02T06:27:52.647Z","spanId":null,"contributor":null,"audiences":[{"kind":"public","createdAt":"2026-09-19T05:12:29.785Z"}],"fingerprint":"2d6e054196c780c871563c31bb2cac81e9e2ed5e0565702ff6f66249f7f67ec3","revisionNo":"14","structureHash":"71b94e3a22bfa7bc73869e28c33091763f03247728940c06dcbf479f6d75fe01","page":"https://lattris.com/by/b/memories/3756718a-ef21-427f-ad5f-b4f9bfebc398"},"links":[{"direction":"out","verb":"references","reason":"only if you are about to write the step-3 Jev call and need the current call shape","hash":"35f4f11d64ce4484cbffe65657b28dde5c19ee462b9d2f7e4018d0243c9b14ac","node":{"id":"8c85109d-51c6-43bc-9ab4-98a5f4b02d00","handle":"8c85","type":"note","title":"How to set up Jev","aiSummary":"Get a TypeSafe key and keep it out of your shell history, install the typesafe-sdk package and TypeSafe's Claude Code skill, learn the call shape from the installed package's own help rather than from any page, the four things to find there before your first call, and the smallest call that proves you are set up.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/8c85109d-51c6-43bc-9ab4-98a5f4b02d00?links=1","fingerprint":"e58b9f9428d50daee5533ce4832baa9f33342660ff5ac6a6b147558f412407be","revisionNo":"7","structureHash":"e598ec0fb6d1a55e11dfa63658c14c60f18b0a630b0a9797f1944afed800abc2","intent":"recipe","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/8c85109d-51c6-43bc-9ab4-98a5f4b02d00"}},{"direction":"in","verb":"references","reason":"only if the summary must keep the person's own words and you can score each claim against a purpose: the three-context pattern","hash":"8f74acc83cdab0bdd953782181348dad664e781a15e492c366eba8746c765e1f","node":{"id":"724bd8e0-30c4-46d6-be1a-0f555af0ed2f","handle":"724b","type":"note","title":"Pattern: the clean summary","aiSummary":"Summarise from the transcript, not from memory. Save the JSONL transcript to a file, hand it to a fresh agent that weighs what the person said and treats later messages as corrections, then hold its view against the conversation and surface mismatches. Can be turned into a skill.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/724bd8e0-30c4-46d6-be1a-0f555af0ed2f?links=1","fingerprint":"959d8314ef31d870ab2146f31262a660d5fc85e4bd664334eb6c064c680e69ba","revisionNo":"2","structureHash":"05fad57f91238af5fe504fa2b52a9e4e35eb5d7ae0e7225ad6f59750c3ae3422","intent":"recipe","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/724bd8e0-30c4-46d6-be1a-0f555af0ed2f"}},{"direction":"in","verb":"references","reason":"only if you need a record of what a person said in a conversation, without your own reading of it mixed in","hash":"edb8c68a8ee8a34e71b43ba5b46b9fc1b22f241e07be2adc0f4f97c55abf1b7f","node":{"id":"f0f5decc-32a9-49de-ad3d-0ea1871263fc","handle":"f0f5","type":"note","title":"Jev picks from answers you write: when to use it and how to word the questions","aiSummary":"Jev, TypeSafe's decision model, picks from answers you write and gives each one a probability. Use it when the same judgment is needed many times and you can write every answer down first. Read the spread of probabilities to find the unsure answers. Word each answer tightly, and ask first whether the question applies, because Jev picks a wrong answer when the right one is missing. A person still decides what matters, because some picks are wrong and Jev does not learn from its mistakes.","erased":false,"href":"https://lattris.com/api/lattris/by/b/memories/f0f5decc-32a9-49de-ad3d-0ea1871263fc?links=1","fingerprint":"43a30188c8bed6cb34e1c429c68b09aff611314c63650658756f1563927cdb67","revisionNo":"13","structureHash":"0df22d1c0fbb9aa7e1ba50bd4a7eab4f3b9a833a10eec51ab1aea1289522cef3","intent":"truth","authorName":"Vincent Yeung","page":"https://lattris.com/by/b/memories/f0f5decc-32a9-49de-ad3d-0ea1871263fc"}}],"dictionary":{"lattris":"A personal knowledge base — how a person captures what they know as linked blocks they own and control. To understand how this knowledge base works: https://lattris.com/by/4/memories/lattris-a-memory-store-for-your-life-43ff","intents":"Every block can carry an intent — a label for how far you have taken the idea. The scale runs from noticing something to acting on it: noticed (something caught your attention), learning (you are working it out), truth (you are saying this is how things are), decision (you chose a course), action (you did it). Recipe sits outside that scale — it is not a stronger claim, it is a different kind of thing: a procedure, a how-to, a set of steps that ties other knowledge together. Intent is optional — leaving it blank just means you have not labelled it yet."},"instructions_to_ai":"This is a recipe from a personal knowledge base. Read it and apply it to what the person you're talking to is working on. Follow the linked blocks to explore further related topics if it might be useful to the user; open each at its page address. Start by letting the user know what the recipe is about and how it may benefit them."}