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AI Overview

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Most LLMs use a combination of heavily overlapping public datasets, meaning their core knowledge bases are very similar. However, each company also uses distinct proprietary data, filtering techniques, and fine-tuning methods that give their models unique capabilities and conversational styles. [1, 2]
 
Why They Feel the Same
  • Shared Web Crawls: Nearly every major LLM (like OpenAI’s models, Anthropic’s Claude, and open-source models) trains on massive internet databases like Common Crawl, as well as open repositories like Wikipedia and GitHub. [1, 2]
  • Statistical Convergence: Because they all read roughly the same public internet history, they predict the next most probable word based on the same shared facts, leading to similar standard answers. [1, 2]

Why They Are Different
  • Proprietary & Licensed Data: Companies differentiate their models by paying for exclusive datasets (e.g., specialized news archives or academic databases) that are not freely available on the open web. [1]
  • User/Product Data: The most valuable data comes from the AI companies’ own ecosystems (e.g., chat logs, system feedback loops), which trains the AI on how to interact effectively with humans. [1]
  • RLHF (Reinforcement Learning from Human Feedback): After learning the facts, models are “aligned” using human raters who tell the AI which answers are best. This heavily dictates the tone, safety, and personality of a specific model. [1, 2]
  • Web Searching (RAG): When you ask an LLM a live question, many use search tools (like Google or Bing) to pull real-time information to supplement their static, pre-trained knowledge base. [1, 2, 3, 4, 5]

If you’re trying to figure out which model to use, tell me:
  • What specific tasks or questions are you asking the AI?
  • Are you looking for creative writing, coding, or factual research?

I can help you narrow down exactly which LLM best fits your needs.
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End of AI generated info.
Fred The Submarine Guy Raley
 

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