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I help executives and key stakeholders with AI driven strategies that enhance human decision making, optimize performance, and drive sustainable growth. | 20+ Years in Tech | Founder at Stratishield
James Betker, research engineer at OpenAI, wrote in his blog: “It’s not the model weights that you are referring to. It’s the dataset.” He claims that almost all LLMs will eventually produce similar responses if you feed them the same data. That’s why you should always find out the source your AI vendor uses for their tool. Is it a general tool making big claims, or has it been trained on niche data to solve specific problems? Because the data you feed is the only differentiator. The output quality depends entirely on what goes in—high-quality, relevant data creates niche tools that solve niche problems. If the data is poor, the AI’s performance will be poor too. My advice after integrating 100+ AIs into businesses: 𝗗𝗼𝗻'𝘁 𝗺𝗲𝘀𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗶𝗳 𝘁𝗵𝗲𝘆 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀. If your AI vendor uses incomplete, outdated, or irrelevant data, the AI’s output will be just as useless. The quality simply won’t be there. In other words, garbage in, garbage out. PS. For more tips on practical AI applications for your business, follow Benjamin Bohman
ai without clean data is like a chef with spoiled ingredients. trash in, trash out.
Marketer, driving 20M in sales through strategic creative | I create breakthrough marketing for results-driven leaders
1moGreat advice! Quality data is everything when it comes to AI performance. It's not just about the tool, it's about the data it's trained on. And yes, ensuring that the AI has access to high-quality relevant data sources is key to getting the results you need. Without that foundation, the AI won't deliver the value you're hoping for. So true, Benjamin Bohman!