M&A Science's "intelligence hub" differentiates from generalist AI like ChatGPT by grounding answers in a closed ecosystem of 400+ expert interviews. It provides sourced, experiential intelligence rather than generic internet-scraped guesses, making it a reliable tool for high-stakes professional work.

Anthropic's chatbot excels at writing because it was 'fed' high-quality books, while Elon Musk's Grok is crude from a 'diet' of tweets. This demonstrates that the quality and nature of input data directly shape an AI's output, skills, and personality. Your model becomes what it consumes.

To create a reliable AI persona, use a two-step process. First, use a constrained tool like Google's NotebookLM, which only uses provided source documents, to distill research into a core prompt. Then, use that fact-based prompt in a general-purpose LLM like ChatGPT to build the final interactive persona.

Instead of prompting an AI to generate a full article, which often results in 'slop,' a better approach is to use it as an assembly tool. Feed the AI granular, pre-vetted pieces of unique business intelligence (like sales data or expert insights) to construct a higher-quality output.

Microsoft's research found that training smaller models on high-quality, synthetic, and carefully filtered data produces better results than training larger models on unfiltered web data. Data quality and curation, not just model size, are the new drivers of performance.

Claude's proficiency in writing is not accidental. Its development, backed by Amazon's Jeff Bezos (who owns The Washington Post), involved training on high-quality journalistic and literary sources. This strategic use of superior training data gives it a distinct advantage in crafting persuasive prose.

Research shows that AI models trained on smaller, high-quality datasets are more efficient and capable than those trained on the unfiltered internet. This signals an industry shift from a 'more data' to a 'right data' paradigm, prioritizing quality over sheer quantity for better model performance.

AI-generated "work slop"—plausible but low-substance content—arises from a lack of specific context. The cure is not just user training but building systems that ingest and index a user's entire work graph, providing the necessary grounding to move from generic drafts to high-signal outputs.

Move beyond the prompt by creating local folders containing brand guidelines, founder writing samples, ICP lists, and case studies. When your AI agent can access these files, its output transforms from generic to highly usable and on-brand, dramatically improving quality.