Why does Gemini forget my personal preferences?

Gemini forgets your personal preferences because personalization stores a fuzzy profile from past chats, not explicit rules, and it requires Keep Activity, a personal Google account, and is excluded from Temporary Chats. There is no preferences panel where you can save "always use metric" the way you would save a font choice. A memory layer that holds explicit rules fixes this.

Three causes feed the preference drift.

1. Personalization is inferred, not declared. Google's personalization feature watches your past chats and builds a model of you. It is good at picking up that you write in plain English. It is unreliable at locking in hard rules ("always use metric, never use Fahrenheit") because the model treats them as patterns, not constraints.

2. There is no explicit preferences store in the Gemini app. Unlike Custom GPTs or Claude's Styles, Gemini does not surface a "saved info" panel where you can type explicit preferences. The closest equivalents — Gems — are persona-and-knowledge bundles, not preference settings.

3. Eligibility rules limit who gets even the inferred memory. You need to be 18 or over, signed in with a personal Google Account, and have Keep Activity on. Work, school, and supervised accounts do not get personalization, and Temporary Chats are excluded by design. If you mostly use Gemini for work, preference memory is effectively off.

So even when Gemini "knows" you, the knowledge is fuzzy, account-wide, and conditional.

The drift looks cosmetic and adds up:

• Same correction every week. "Metric units, please" turns into a Tuesday ritual, then a Thursday ritual.
• Format guarantees are unreliable. A standing rule like "no bullet lists" survives a few chats and then quietly returns.
• Work accounts get nothing. If your Gemini lives on a Workspace account, personalization is entirely off, and every chat starts from default behavior.

Google has shipped two near-misses.

Personalization with memory of past chats is the headline mechanism. It is on by default for eligible accounts and you can manage what Gemini remembers from the personalization help article. The limit is that it is theme-level and account-wide; you cannot pin explicit constraints with high reliability.

Gems let you bake instructions into a saved persona ("Always answer in metric units, plain English, no bullet lists, formal register"). Inside that Gem, preferences are stable. Outside the Gem, in a default chat, the rules do not apply. You have to remember to open the right Gem every time.

Both help. Neither gives you a real, model-agnostic preferences store.

Personal preferences are not just for one AI. You probably want the same rules in Claude, ChatGPT, and Grok too. Re-teaching "metric, plain English, no bullets" inside each tool is the kind of low-grade friction that adds up to hours per month.

The cleanest fix is to keep preferences once, somewhere model-neutral, and let every tool read from it.

MemoryLake stores explicit preferences as durable memory, then surfaces them to Gemini and every other AI.

• Explicit, declared preferences. Add "metric units, plain English, no bullet lists" as Memories notes. They are stored as rules, not inferred from chat patterns, so Gemini honors them on turn one.
• Works on work accounts too. MemoryLake sits outside the Gemini personalization eligibility rules, so the same preferences apply whether you are signed into a personal or a Workspace account.
• Same preferences in Claude, ChatGPT, Grok. Define them once. Every AI you use reads from the same Project, so corrections in one tool flow to all the others.

MemoryLake scored 94.03% on the LoCoMo long-context benchmark, the top published result as of 2026, with millisecond retrieval and AES-256 end-to-end encryption.

1. Create a Project for your preferences. Sign in to MemoryLake, open Project Management, click Create Project, and name it "Personal preferences". Add each preference as a named entry in the Memories tab — short rules work best ("Use metric units", "British English spelling", "No bullet lists unless asked"). Use the Document Drive only if you have a longer style guide you want available too.
2. Generate an MCP Server endpoint. Open the MCP Servers tab, click Add MCP Server, name it "Gemini preferences", and click Generate. MemoryLake returns the API key ID, secret, and endpoint URL. Copy the secret immediately — it is shown only once.
3. Connect Gemini. Gemini does not natively support MCP, so use the REST API with your Bearer token to load preferences at the start of each chat, or paste a one-line system prompt that points Gemini at your MemoryLake project ID. Developers can integrate the Python SDK so every Gemini call hydrates the preferences automatically.