How To Gather Real AI Prompts * There is much focus on how to optimize brands for particular prompts in AI. However, most people guess what prompts they should optimize for. This can call the entire strategy into question. * Outside of Similarweb, in our opinion, there are no reliable data sources that show users' real prompts. Existing tools are guessing based on dubious underlying data. * We propose a way to gather real user prompts in AI at scale for free. * Guide: graphite.io/five-percent/res… How Did You Hear About Us? * Attribution in AI is also hard. One solution is to ask users to self-report the channel primarily responsible for their conversion. This is often done by asking “How Did You Hear About Us?” * Self-reporting can also be extended to gather more information, namely, real user prompts. What Prompt Did You Use? We built a new conversion funnel at Graphite that asks the following questions. 1. How Did You Hear About Us? (channel) 2. What LLM Did You Use (product) 3. What prompt did you enter (prompt) This now tells you not only whether a user entered via AI, but also which LLM they used and the prompt they entered. Results * We now see 30 % of inbound leads come from AI. 52% came from ChatGPT and 40% from Claude. * We receive 10x more referral traffic from ChatGPT than Claude, while the % of leads that self-report Claude is almost as high as ChatGPT. This suggests conversions via Claude are particularly underreported using last-touch, far more than ChatGPT. * AI leads tend to be less qualified than other channels like referrals. * Only 21% of leads that self-report as coming from AI appear as referral traffic from AI. The rest came from search (49%) and direct (30%).