Qian

我不得不说，感觉Claude和ChatGPT已经像是两个时代的产物了。我是再也用不回ChatGPT了。

Introducing Cowork: Claude Code for the rest of your work. Cowork lets you complete non-technical tasks much like how developers use Claude Code.

You should NOT use LLMs to generate synthetic human-like profiles. I just read the NeurIPS paper "LLM Generated Persona is a Promise with a Catch" and it confirms a suspicion we’ve held for a long time: You cannot "invent" a realistic human being using just statistics and an LLM. Yes, they are more scalable and cost-effective alternative to human interviews to create digital expert personas but this paper also proves that these synthetic profiles contain systematic biases that skew simulation results away from real-world outcomes. The more creative freedom you give an LLM to generate a persona’s backstory, the further it drifts from reality. Another important finding is that as LLM-generated content increases, simulated personas shift progressively toward left-leaning stances. LLMs also systematically generate personas with overly optimistic outlooks, using positively valenced terms like "love," "proud," and "community" while omitting life challenges or negative experiences. This emotional bias is horrible for strategy and creativity-related decision-making tasks! If you are building AI agents for strategy or decision-making, you don't want an idealized "Yes Man." This is why I keep posting about the importance of Tacit Knowledge, Context Engineering, and AI Interviewer to extract human knowledge. The research paper critiques the practice of "inventing" people from statistical margins (Census data + LLM imagination), whereas the system should focus on "extracting" people from ground truth (Real Expert + Interview). After testing and evaluating LLM personas generated by public datasets, we observed that they are not ready for production AI agents. That's why my focus is on building an interviewer experience that extracts as much learning as possible from the human expert, and creating a context system that grounds that expert's outputs in truth; using a real-time, long-form interview to capture "implicit knowledge" and "distinctive methodologies". Another architectural difference that I find is relying heavily on single-pass prompting. They feed demographic data into an LLM and ask it to generate a "Descriptive Persona" (a narrative bio). They found this introduces massive bias. To address these critical flaws in the current persona generation, I propose the following to resolve or at least mitigate these specific issues: 1. Addressing the "Joint Distribution" Issue: Researchers report that they cannot precisely simulate an individual due to fragmented datasets (e.g., they have data on "Income" and "Education" separately but lack information on their overlap for a specific person), resulting in "incongruous combinations." By interviewing a real human, you capture the natural joint distribution of their beliefs. You don't have to guess if a "high-income expert" cares about "sustainability"; the expert tells you. We need to bypass the statistical reconstruction problem entirely by building scalable interviewer solutions. 2. Avoiding "Positivity Bias" & "Leftward Drift": The paper proves that when LLMs are asked to write a persona description (Descriptive Persona), they default to "pollyannaish," overly positive, and politically progressive profiles. The interviewer system should be designed to gather insights into "mistakes," "judgment," and "distinctive methodologies" rather than generic best practices. By forcing the model to ingest a transcript of hard-won lessons and failures, you will override the model's default tendency to be "nice" and "generic." The paper also mentions a lack of "ground truth" to validate if a persona is accurate. My solution includes a built-in validation loop where the human expert reviews and scores the output. This "Human-in-the-Loop" verification is exactly what the researchers argue is missing from the field. "Descriptive Personas" generated by LLMs are articulate but statistically flawed. To scale true expertise, we must stop trying to simulate people and start interviewing them.