Product
When a question deserves deep research, not a chat reply
Ask a chat model about a market, a regulation, or a competitor and you will get a confident answer in seconds. That answer is a first draft from memory, useful for orientation, dangerous for decisions. Deep research is the other tool: it plans a search path, gathers live and uploaded sources, compares claims, and returns a structured brief with citations. Knowing which tool a question deserves is one of the highest-value judgment calls in an AI workspace.
What actually differs under the hood
A chat reply is a single pass: the model answers from what it learned in training, possibly months ago. A deep research run in ChatLite is a process: the question is decomposed into sub-questions, each one drives searches against live sources and any documents you attach, conflicting claims are weighed against each other, and the output arrives as a structured report where every substantive claim carries a citation you can check. It takes minutes instead of seconds, because it is doing what a diligent analyst would do, just faster.
Reach for chat when…
The question is about understanding rather than facts: explain a concept, restructure your argument, draft a document, critique a plan, transform something you already have. Chat is also right when you supply the source material yourself; summarising a contract you attached is not a research problem, because the ground truth is in the conversation.
Reach for deep research when…
The answer depends on the current state of the world, and being wrong has a cost. Market sizing before a board meeting. What a new regulation actually requires of your product. How three vendors compare on the dimensions you care about. What changed in a competitor’s pricing since last quarter. The common thread: you would not accept these answers from a colleague without sources, so do not accept them from a model without citations.
The failure mode to avoid
The expensive mistake is not choosing the wrong tool once; it is making chat answers load-bearing. A statistic from a model’s memory gets pasted into a deck, the deck gets forwarded, and three weeks later a customer asks for the source. Teams avoid this with a simple norm: anything quoted onward needs a citation, and citations come from research runs or web search, not from memory. The inverse mistake is cheaper but real: running a twenty-minute research job on a question chat would have answered well enough. Orientation questions do not need a bibliography.
The practical rule of thumb we suggest: if the answer will leave your screen (into a decision, a document, or someone else’s inbox), give it sources. If it stays in your head as understanding, chat is exactly the right tool.