A leading AI research organization is inviting advanced LLM power users to evaluate how effectively AI systems handle personalized, real-world life tasks. The goal is to go beyond “Did it sound smart?” and instead answer a more practical question: “Did it actually help me succeed in my real situation?”
You’ll be assessing AI behavior on tasks that look like what people do every day—things like planning, choosing between options, troubleshooting constraints, and making realistic decisions based on personal context. Your work helps researchers understand where current systems perform well and where they fall short in ways that matter in real life.
Who We’re Looking For
Strong candidates typically have deep, ongoing experience using LLM tools in personal life—not just occasional testing, but regular use with clear personal stakes. The best matches can evaluate an AI system’s outputs with a critical, realistic lens.
We’re looking for people who:
Use LLMs heavily in their own daily lives.
You’ve built habits around using tools like ChatGPT, Claude, Gemini, Perplexity, or AI coding/agent tools.
You understand what kinds of assistance feel genuinely valuable versus generic or repetitive.
Can handle multi-step work, not just single questions
You’ve used AI for planning and research.
You know how to iterate with AI (give feedback, refine constraints, correct assumptions).
You can evaluate whether the final result is coherent, complete, and usable.
Have experience with workflows and agent-style tools
Familiarity with tools such as ChatGPT, Claude, Gemini, Perplexity, Cursor, Windsurf, Codex, or similar AI assistants.
You understand how agent tools behave when tasks require multiple actions, tradeoffs, or decision points.
Can clearly explain why an answer is good—or why it fails
You can judge usefulness, personalization, practicality, and realism.
You can identify issues like missing context, hallucinations, unsafe advice, overconfidence, or unrealistic assumptions.
You can point out whether something is incomplete, ambiguous, or likely to lead to failure.
Have strong written judgment and attention to detail
You can communicate your evaluation clearly and specifically.
You pay attention to correctness, safety, nuance, and whether the output matches real constraints (time, budget, preferences, goals, tools, environment).
Why This Work Matters
LLMs are quickly becoming personal assistants for everyday decisions—but most current outputs still struggle with real-life context. For AI to be truly helpful, it needs to do more than generate plausible text. It should:
Understand personal preferences and goals
Work within constraints (budget, time, physical limits, schedule, available resources).
Recognize tradeoffs and explain them clearly.
Make progress toward a specific definition of success
Behave safely and realistically, especially when advice affects health, productivity, or major decisions.
Your evaluations will help improve how AI systems support people with high-context, practical tasks across areas such as food, health, productivity, careers, and learning. Ultimately, this work contributes to building AI assistants that are more personalized, trustworthy, and genuinely effective for day-to-day personal workflows.
