AI Workflows · Guide
Build a Source-First AI Literature Scan for Work
Use AI to organize a small evidence set without letting a smooth synthesis replace the underlying documents.
Teams often need to understand a field before making a decision: what has been tried, where evidence is thin, and which questions deserve a deeper investigation. AI can help sort and summarize a collection of documents, but a fluent overview can make weak evidence look settled. A useful literature scan keeps the source set visible throughout the process. This is a lightweight method for workplace research, not a substitute for a formal systematic review. It is best for an early decision such as choosing what to pilot or what to ask an expert. If the result will support a medical, legal, or other high-stakes decision, use the review method and qualified expertise that decision requires.
Frame a question small enough to answer
Start with the decision, audience, and time horizon. “What are the common ways small teams evaluate transcription quality?” is more workable than “What does the research say about AI?” Define the terms you will search and what counts as relevant evidence. Write down exclusions, such as sources that discuss only marketing claims or a different use case. Ask AI to suggest synonyms and related concepts, then review them yourself. Keep the final search strings and date. This log matters because the set you find depends on your query and the sources available to you. A summary without a search record can appear more complete than it is.
Illustrative prompt · adapt before use
Using only this screened source set and extraction table, draft a decision brief. Give a source location for each claim, keep conflicting results separate, and name missing evidence without inventing studies.Screen sources before summarizing them
Collect titles, authors, dates, links, and short notes in one table. Read the abstract or opening and decide whether each source belongs. Mark primary studies, documentation, independent commentary, and promotional material separately. These types can all be useful, but they answer different questions. Do not let an AI summary collapse them into a single line of “evidence.” For a small scan, a dozen well-chosen documents may be more useful than a hundred unreviewed results. Note why you included or excluded borderline items. If an important paper is inaccessible, mark that limit instead of relying on a snippet that may omit its method or conclusion.
Extract claims with source locations
Use a claim table with columns for finding, source, page or section, method or context, and caveat. AI can draft rows from documents you supply, but verify each row against the original. Check whether a number is a measured result, an estimate, or a claim repeated from another source. Keep quotations short and precise. Look for missing context: sample size, population, comparison, and the conditions under which the result was observed. A workplace pilot from a large organization may not transfer directly to a solo creator. The extraction step is where a confident but unsupported generalization can be caught before it enters the prose.
Synthesize agreement and disagreement
Group findings by the question they address, not by how often an AI model repeats a phrase. Write what several sources support, what they dispute, and what remains unknown. Disagreement may reflect different methods or contexts rather than a failure to find the one correct answer. State the limits of your search so the reader knows how far the conclusion travels. End with decision options and the evidence needed next. For example, a scan might suggest testing two workflows with local recordings instead of claiming one tool class is universally better. The goal is to make a better next decision, not to make uncertainty disappear.
Keep a transparent search and exclusion record
A source-first scan is not a comprehensive systematic review, but a reader should still understand how the source set was assembled. Write the search question, where you searched, the main terms and synonyms, the date, and the inclusion boundary. Then record why candidate sources were excluded: wrong population, unrelated outcome, commentary rather than original study, duplicate report, unavailable full text, or insufficient method detail. The log prevents AI from presenting the first few convenient results as the whole field. It also tells a later reviewer what to revisit when the question changes. Use AI for clerical help only within the supplied candidate set. It can propose tags and surface likely duplicates, but a person checks titles, abstracts, methods, and full text where a claim depends on them. Never ask it to invent missing studies or infer a paper's conclusion from a headline. If a source is behind a paywall and you cannot read the needed section, label the limitation. The honest conclusion may be that the scan found promising leads but cannot support a firm recommendation. This is still useful for deciding the next research step.
Compare methods before combining findings
Two papers may use the same label for very different tasks. One may study novice users in a controlled setting; another may report a small workplace deployment. Before synthesizing, note population, intervention, comparison, outcome, duration, and relevant limitations in a compact evidence table. Do not merge numbers across studies unless the measures and context justify it. Ask whether the study tested the mechanism your question actually concerns. An AI summary may flatten all findings into a common theme because shared wording is easier to detect than methodological difference. For a worked example, imagine searching for evidence that an AI writing assistant reduces editing time. One study measures minutes to produce a first draft; another measures time until a fact-checked article is approved. Both use the phrase “time saved,” but their endpoints differ. A useful brief states the difference rather than averaging them into one confident estimate. It might say that the studies suggest faster first drafts while leaving final review cost uncertain for the target team. The reader can then design a local test of the missing endpoint.
Turn the scan into a decision brief
Organize the output around the decision: what is reasonably supported, where results conflict, what the evidence does not cover, and what local observation would reduce uncertainty. Give every material claim a source and location so the reviewer can open the relevant figure or passage. Keep direct quotations short and exact; do not let AI create quotation marks around a paraphrase. Separate study authors' findings from your interpretation. If a conclusion depends on an assumption about your own team, write that assumption plainly. A useful prompt is: “Using only these supplied papers and my extraction table, draft a brief answering the stated question. For each claim, cite the exact paper and page or figure; keep conflicting results separate. Mark missing details and do not supply outside studies from memory.” The model's answer then becomes a draft to audit, not the source of truth. If the review finds no suitable evidence, write that result and propose a narrow local test or expert question. The scan has succeeded when it makes the decision and its uncertainty clearer.
Know what the scan cannot establish
The scan may miss relevant work because of search terms, language, access, publication venue, or the team's available time. State those limits alongside the conclusion. Do not turn “we found no supporting source in this small set” into “no evidence exists.” Likewise, several papers repeating a claim do not make it applicable to your organization if they studied different users or outcomes. The brief should identify what evidence would change the decision: a larger review, a specialist's interpretation, a small workplace trial, or better measurement of the actual workflow. Keep the original sources and extraction notes so another person can inspect the argument. If a source is corrected or withdrawn, the team should know which claims depend on it. The practical result is a decision with a trail: the question, the sources screened, the claims supported, the gaps, and the next action. AI can accelerate organization of the material, but readers must be able to follow the evidence without trusting the model's memory. Try this next: write one work question, collect eight sources with inclusion notes, and ask AI to propose themes only after you have verified a claim table from the original documents.
Sources and update note
Follow the linked official source before a product, price, plan, or policy decision.