Find
Retrieve the right candidates from the web or another approved source before asking a model to explain anything.
A confident answer is not automatically a good answer.
Research is where eFind is allowed to be uncertain on purpose.
The job is to turn a broad question into things we can test, measure, reject, improve and eventually—if the evidence is good enough—build into a product or infrastructure decision.
Curiosity needs a method.
The topics below describe areas of investigation, not guarantees about a future product.
Search a changing web and retrieve information relevant to the actual question.
Distinguish original reporting, primary documents, commercial material and derivative summaries.
Bring sources together while keeping attribution and uncertainty visible.
A map, video, business result or saved plan may be the useful next step.
Retrieve the right candidates from the web or another approved source before asking a model to explain anything.
Order sources and evidence around relevance, quality, freshness and the person’s actual question rather than popularity alone.
Make generated explanations depend on evidence and preserve links back to original material so a polished paragraph never becomes its own source.
Measure whether the system found the right material, represented it accurately, handled disagreement and knew when there was not enough evidence to answer well.
Search intelligence should combine retrieval and generation without blurring the line between them. The answer can become more conversational while the evidence becomes easier—not harder—to inspect.
Conversational interfaces do not remove the need for crawling, indexing, retrieval, ranking, freshness, spam resistance, source quality and understanding what the person actually asked.
If a question depends on the current web, the system needs the current web. Generating from stale internal knowledge with a confident tone is not a search strategy.
Which source appears first can shape what people believe and what businesses receive traffic. Relevance, quality, diversity, freshness and commercial placement need explicit rules rather than one mysterious score.
Search quality is an adversarial problem. Low-quality pages, manipulation, generated content and commercial incentives keep changing, so quality systems have to keep changing with them.