Here’s a 10-second test: ask your AI to recommend one YouTube video, based only on what it remembers about you, and see what comes back.
No genre hints, no “I’m into true crime lately,” no context dump. Just a cold read on your own chat history.
u/WhiplashNinja posted this exact challenge on r/ChatGPTPromptGenius, and it turned into a neat little experiment for checking how much your conversation history actually shapes AI recommendations, versus how much the model is just guessing.
The Prompt 📋
Copy this into whichever AI you’ve got the most chat history with (ChatGPT, Claude, doesn’t matter) and paste it in fresh, no extra setup:
Based on everything you know about me from our previous conversations, recommend me one YouTube video that you think I would genuinely enjoy watching.
Give me one video only. Include the title and a direct YouTube link.
Don’t explain your reasoning. Skip the preamble. Just respond with the link.
Run it in a brand new chat, not one where you’ve already been talking about videos or hobbies today. You want the model pulling from long-term memory or your chat history, not from the last five messages you happened to send it. If the tool asks a clarifying question before answering, that’s already a data point: it means the model isn’t confident enough in what it knows about you to commit.
Why It Works 🧠
Three small moves are doing all the heavy lifting here.
“Based on everything you know about me from our previous conversations” forces the model to pull from your real chat history instead of defaulting to a generic, crowd-pleasing pick. It’s a memory-retrieval trigger wearing a casual outfit. Without that line, most models default to something safe and broadly popular, a MrBeast video or a top-of-trending explainer, because that’s the statistically likely answer for “a person on the internet,” not for you specifically.
“One video only” plus “don’t explain your reasoning” kills the model’s habit of hedging with three safe options and a paragraph of disclaimers. You get one committed answer. That’s the whole point: a wishy-washy list of options tells you the model doesn’t have a real read on you yet. Models are trained to be helpful and thorough, which usually means covering their bases with multiple choices. Forcing a single pick strips that safety net away and makes the model actually commit to a guess about who you are.
“Skip the preamble” wipes out the “Great question! Here’s a video I think you’ll love” opener. Clean output, nothing to wade through. It also removes an easy place for the model to smuggle in a hedge, since a lot of the wiggle room in AI answers lives in that opening sentence.
What the Results Mean 🎯
Here’s where the original post gets fun. The rule is you comment back on whether you watched the video, liked it, or refused to watch it at all.
A good rec means the model has been paying more attention across your conversations than you gave it credit for. People running this test have reported getting recs tied to a random side project they mentioned months ago, or a hobby that came up once in a totally unrelated thread. That’s the model connecting dots you forgot you dropped.
A miss tells you it’s been skimming instead of building a real picture of your taste. This happens more than you’d think, especially if most of your chat history is task-focused (debugging code, drafting emails) rather than personal. The model has plenty of data about how you work, but almost nothing about what you’d want to watch for fun, so it falls back on generic guesses dressed up as personalized ones.
A flat-out refusal is its own data point too. Chances are the model latched onto one weird tangent from three weeks ago and ran with it like it was your whole personality. Maybe you asked one question about sourdough starters and now you’re getting bread science documentaries recommended with total confidence. That’s a useful, slightly humbling reminder that AI memory isn’t a nuanced psychological profile, it’s pattern-matching on whatever stood out.
Extra Tips 💡
- Run the exact same prompt on two different AI tools and compare picks. The gap between them tells you more than either answer alone. If ChatGPT and Claude land on wildly different videos, that’s a sign the “personalization” is more vibes than substance.
- If the rec feels way off, follow up with “what in our conversations made you pick that?” Now you get the reasoning you skipped the first time, and you can spot exactly where it went sideways. Sometimes the answer is genuinely surprising, and sometimes it’s a single throwaway comment blown way out of proportion.
- Rerun it in a month. Recommendation quality should shift as your history grows, and tracking that drift is basically a free check on how well the memory actually works. If the pick doesn’t change at all after weeks of new conversations, that’s worth noticing too.
Prompt of the Day: “Based on everything you know about me from our previous conversations, recommend me one [book / podcast / restaurant] you think I’d genuinely enjoy. One option only, no reasoning, just the answer.”
Run It and Report Back 🏴☠️
Try the prompt, watch (or don’t watch) whatever it hands you, then go compare notes with everyone else running the same test on r/ChatGPTPromptGenius. The results people are posting are all over the map, and that’s kind of the whole point.
Prompt Game Round 2 (YouTube)
by u/WhiplashNinja in ChatGPTPromptGenius