A blank chat box is a poor research tool — it gives you a wall of prose with no structure and no signal about what is solid versus shaky. This assistant wraps your question in structured-output instructions and runs it on your own OpenAI or Anthropic key, returning a sectioned answer that tells you what is reliable and what to double-check.
How it works
Pick a provider and model, paste your API key, and choose a depth and output format. Enter your research question and the tool builds a prompt that asks the model to answer at the depth you set, format it as the shape you chose, distinguish established facts from contested points, and close with a “Caveats & what to verify” section. It is explicitly told not to fabricate statistics, dates, or citations. The tool makes one direct request to the provider and returns the full answer to copy.
For Anthropic, the request includes the official direct-browser-access header so it works straight from the page.
Getting trustworthy answers
The most important habit with any LLM research is to treat the output as a structured starting point, not a verdict. The caveats section exists to point you at the claims worth confirming against primary sources. Use the depth control deliberately: an overview is enough to orient yourself on an unfamiliar topic, while a deep dive is worth the extra tokens when you need to understand competing positions before forming your own view. Phrasing your question precisely — naming the timeframe, the context, and the decision you are trying to make — produces noticeably sharper answers.
Notes and limits
Because the model has no live web access, anything that changed after its training cutoff may be wrong or missing, and it cannot give you working citations. It can also state plausible-sounding falsehoods with confidence. This tool reduces that risk by forcing structure and explicit caveats, but it does not eliminate it. For anything consequential — legal, medical, financial, or factual claims you will publish — verify against authoritative sources before relying on the answer.