Fix AI Date Hallucination

Why models get dates wrong

A language-model response cannot be assumed to carry a live, independently sourced clock. When a user asks "what year is it?" without an external reference, the answer may be inferred from patterns in training material that stopped accumulating months or years before the conversation.

The result: the model confidently states an incorrect year, announces that software which shipped six months ago "hasn't been released yet," or calculates ages and deadlines from a stale reference point. The risk is systematic rather than random: the application has not supplied a current reference or evidence boundary.

The fix: prepend a time-grounding block

Call /v1/calibrate once at the start of every conversation. It returns calibrateBlock — a short plain-text string with an anchored current date and exact UTC time. Prepend it to your system prompt to give the model an explicit temporal reference. Refresh it for long-running sessions. Current factual claims still require sourced evidence, and unsupported or contradictory relationships should be treated as unknown.

// Call once per conversation, before sending the first message.
// Works with any model: GPT-4o, Claude, Gemini, Llama, Mistral, …

const timezone = Intl.DateTimeFormat().resolvedOptions().timeZone;
const { calibrateBlock } = await fetch(
  "https://api.temporalblock.com/api/v1/calibrate",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "X-API-Key": process.env.TBLK_API_KEY,
    },
    body: JSON.stringify({ timezone }),
  }
).then((r) => r.json());

// calibrateBlock is a plain string — prepend it to your system prompt.
const systemPrompt = calibrateBlock + "\n\n" + yourExistingSystemPrompt;

Model-agnostic. Works with GPT-4o, Claude, Gemini, Llama, Mistral, and any other model that accepts a system prompt. No SDKs or plugins required — just a fetch call.

Also pull in live web context

For topics that evolve after the training boundary — current events, software versions, prices, people — combine calibration with a live web search in one call. /v1/full runs both legs and returns two ready-to-use blocks: calibrateBlock and bridgeBlock. BYO your own search-provider key and the search cost is yours directly.

// /v1/full = calibrate + live web context in one round trip.
// BYO your own Perplexity / Brave / OpenAI key for the search leg.

const result = await fetch("https://api.temporalblock.com/api/v1/full", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "X-API-Key": process.env.TBLK_API_KEY,
  },
  body: JSON.stringify({
    timezone: Intl.DateTimeFormat().resolvedOptions().timeZone,
    syncTier: "snippet",
    syncProvider: "perplexity",
    syncApiKey: process.env.PERPLEXITY_API_KEY,
  }),
}).then((r) => r.json());

// result.calibrateBlock → prepend to system prompt
// result.bridgeBlock    → append or inject as a user turn

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