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Use AI to get oriented; use independent research to establish what is true. AI can help frame a question, explain unfamiliar ideas, summarize material you provide, and suggest what to investigate. For current facts, original evidence, or decisions with real consequences, check authoritative sources yourself and bring in qualified expertise when needed. The most reliable approach is usually a combination: let AI help you investigate, but do not let it replace verification.

What AI is useful for—and what it cannot establish

Generative AI is useful for tasks where a plausible draft or starting point saves effort without being mistaken for evidence. The UK House of Commons Library identifies brainstorming, summarizing, generating questions, changing wording, and producing concise explanations as suitable support tasks. You can ask it to explain a term in plain language, outline an unfamiliar subject, or summarize a document you supply.

Those outputs do not establish whether a factual claim is true. An answer can sound fluent and confident while being wrong, incomplete, outdated, biased, or backed by a citation that does not support it. OpenAI’s product guidance puts the distinction plainly: “Use ChatGPT as a first draft, not a final source.” The House of Commons Library similarly advises that “AI should be treated as an assistant, not an authority.”

AI is a weaker choice for definitive factual answers, contested or politically sensitive subjects, legal or policy interpretation, and unsupervised analysis that is expected to be impartial. It can help you understand or organize such questions, but those uses call for careful checks against credible sources and, where appropriate, a human expert.

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When to do your own research

Rely more heavily on independent research when the answer must be accurate, current, traceable, or defensible. That means finding and reading authoritative material, not just asking a different AI system for another answer.

  • You need a current detail. A model without web search may not know about developments after its training data. Search-enabled AI may retrieve newer material, but you still need to check the source and its date.
  • You need original evidence. If a claim depends on a study, law, policy, product specification, or public record, inspect the underlying material instead of relying only on a summary.
  • The claim is consequential, disputed, or surprising. Seek independent confirmation and consult a qualified specialist if the decision warrants it.
  • You must explain or justify a decision. Use sources that can be cited and checked, and take responsibility for how evidence is interpreted.
  • You lack enough subject knowledge to assess the answer. AI output is harder to evaluate if you cannot recognize missing context, an implausible claim, or a distorted summary. Build understanding from reliable sources or seek help before relying on it.

For example, AI can help you list questions to ask when comparing a policy or explain unfamiliar terminology in a document. It should not be the sole basis for deciding what a current rule requires: find the relevant official guidance and confirm the date and scope yourself.

Choose the method by task, stakes, and verifiability

Question AI can help with What needs independent checking
What am I trying to understand? Plain-language explanations, outlines, and questions to investigate. Definitions and interpretations that affect a real decision.
What does this material say? A first-pass summary of text you provide. Whether the summary preserves important qualifications, omissions, and context.
What is true now? Finding possible leads if the tool has current search. Dates, figures, events, rules, and claims checked against current authoritative sources.
What should I decide? Organizing options and identifying factors to consider. Evidence, expert judgment, and accountability for the decision.

For formal evidence synthesis, the bar is higher still. Cochrane’s 2026 guidance says to assess a tool’s purpose, training, testing or validation data, performance in the relevant context, user capability, and transparency or documentation. For large language models and generative AI in evidence synthesis, Cochrane recommends proceeding with mitigations, such as human verification or validation within the review.

A practical workflow for using AI without outsourcing judgment

  1. Define the task. Write down what a correct answer must include, how current it needs to be, and what could go wrong if it is inaccurate.
  2. Give AI a bounded job. Ask for an outline, an explanation, a summary of supplied text, or a list of questions—not an unquestioned final verdict.
  3. Turn the output into claims to check. Flag dates, numbers, quotations, technical statements, and references individually. A mixed answer may contain both accurate and inaccurate details.
  4. Open the important sources. Confirm each source exists, is suitable for the claim, is current enough, and actually says what the AI attributes to it. A citation is a lead to inspect, not proof that the summary is faithful.
  5. Seek a second basis for important claims. For consequential, disputed, or unexpected claims, look for independent confirmation. Ask a subject expert when the decision requires expertise you do not have.
  6. Edit and take responsibility. Restore missing context, represent uncertainty honestly, and follow any applicable rules on disclosing AI use.

This sequence reflects the House of Commons Library’s guidance to define the task, use AI, check claims against reputable sources, seek subject expertise, and then edit and take responsibility. Cochrane adds tool assessment and explicit human oversight for evidence synthesis.

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Do not treat a productivity figure as a general promise

A UK Government and Behavioural Insights Team case study published on 23 April 2025 reported that one AI-assisted evidence review took 23% less time overall than the human-only review in that exercise. The same case study reported 56% less time for analysis and synthesis of selected literature. These are two measurements from one exercise, not independent replications or general estimates of AI performance.

The authors explicitly caution that the case study is not generalisable. The AI draft was less fluent, needed more revisions, and was supplemented by manual checks and edits; errors still required verification. The results therefore illustrate a possible workflow benefit in that specific case, not a guarantee that AI research is faster or better in other tasks.

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Account for privacy, rules, and high-stakes decisions

Before entering information, consider the terms of the particular AI service, what it may do with your inputs and outputs, your organization’s rules, and applicable privacy requirements. UK Government AI Playbook guidance warns that public AI services may leave users with limited control over inputs and outputs. Product terms differ, so do not assume one service’s protections apply to another.

For decisions that can cause significant harm, keep meaningful human oversight and use appropriate expertise rather than treating an AI response as authorization. In evidence synthesis, Cochrane recommends tool assessment and verification or validation. For financial decisions, the Financial Conduct Authority’s guidance is specifically UK-focused: it says general-purpose AI information is not regulated financial advice under the protections it discusses. Its suggestion is to “Use AI to guide you where to dig deeper, not as your final answer.” That statement is consumer guidance for UK investment research, not a universal rule about regulation in other countries.

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