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When is outside research needed, and when does it take the task off course?

B024 · B Providing context · Organizing material and source boundaries

Key answer

Search externally when an answer requires outside facts or verification of truth, completeness or continued validity. For faithful summary, translation, rewriting or analysis of a stipulated setting, handle external facts separately as needed rather than changing the evidence boundary.

Full explanation

Distinguish the purpose:

· Restate material: “What did the author say?” Follow the original and list concerns separately.

· Evaluate material: “Is the author right?” Consult original research, current rules and other checkable evidence.

· Solve a real problem: current product comparisons, for example, require relevant updated information with dates/versions.

Research supports retrieval's value and its selection requirements. Lewis et al.'s RAG work combined Wikipedia retrieval with generation and improved knowledge-intensive tasks including open-domain QA in a trained system. Original study ACL 2025's *The Distracting Effect* studied distracting retrieved passages: irrelevant material can cause errors even when superficially close to the question. Original study

Search around explicit gaps. For a feature, find official documentation for the right product/version. For a paper's findings, read that paper's methods and results rather than another article on a similar topic.

First summarize the article faithfully without presenting external content as the author's view. Then separately fact-check key figures using original sources and dates. If sources conflict, distinguish the original claim from verification results.

You need not ask about internet permission before every task; agree initially which gaps may be researched. If “given material only” is explicit, identify gaps as in B023 and request broader sourcing. The web cannot supply private budgets, intentions or internal records known only to the user.

References

1. Lewis et al. (2020), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, NeurIPS

2. Amiraz et al. (2025), The Distracting Effect: Understanding Irrelevant Passages in RAG, ACL