AI for Creators · Guide
Create a Source Ledger for an AI-Assisted Explainer
Keep every important claim connected to a source, an interpretation, and an editorial decision.
An AI-assisted explainer can be clear, engaging, and wrong in small consequential ways. A definition may be based on an outdated page. A statistic may be repeated without its denominator. A caveat may vanish during a rewrite because it made the paragraph less smooth. The problem is rarely solved by adding “fact-check this” to a prompt. The editor needs a visible path between each significant statement and the material that supports it. A source ledger gives that path a simple form. A ledger is not a bibliography pasted at the bottom of an article. A bibliography tells readers which pages were consulted; it does not show which sentence each page supports or what the editor inferred. A useful ledger lists the claim, the precise source passage, its date or version, the intended wording, and the review decision. It is small enough to maintain while writing. It also exposes unsupported claims before they are polished into the final draft.
Define the claims worth tracking
Start with the reader's question and a short outline. An explainer about choosing an AI transcription tool, for example, might need to explain how to test names, speaker changes, and action items. Some statements are general editorial advice and can be written as recommendations. Others make verifiable claims about a product, standard, legal rule, research finding, or technical capability. Mark the latter for the ledger. The point is not to attach a citation to every connective phrase; it is to prevent an important factual assertion from floating free of evidence. Use a claim ID that survives drafting, such as C1 for a definition and C2 for a comparison. Record the working sentence even if the wording is rough. If the draft says “all transcription tools export speaker labels,” a source that describes one vendor does not support “all.” The ledger should make the mismatch obvious. Change the sentence to a narrower supported claim, find broader evidence, or remove it. A blank source cell is an editorial signal, not an invitation for the model to invent a link. Separate a source statement from an interpretation. A document may say that a feature is available in a specific plan, language, or region. The article may infer that it suits a team with that need. The first is a factual claim tied to a source; the second is an editorial judgment that should name its conditions. Write both in the ledger. This helps an editor notice when a paraphrase has quietly become a recommendation or when a recommendation sounds like a universal fact.
Capture enough of the source to verify it
For each entry, save the source title, publisher, stable URL, relevant section or page, and date or version when provided. Add a short excerpt or paraphrase in your own notes so the reviewer can see why the source was selected. Do not paste a long copyrighted passage into the article draft. If a source changes frequently, note when it was checked. A link to a homepage that requires the reviewer to search again is weak evidence; a direct section or page reference is better. Prefer primary material when the claim concerns a product's documented behavior, a standard, or a study. The product's official documentation is useful for what the vendor says it supports, but it cannot alone prove that the feature performs well for your readers. A study's methods and results are a better source for the study's findings than a social summary. For a practical workflow recommendation, an established accessibility or risk-management guide may offer useful principles, while the article should still explain how the recommendation follows from the reader's problem. A source is an input to judgment, not a badge of authority. Keep uncertainty visible. If the source is a marketing page without test data, label the evidence as a vendor claim. If two official pages describe different availability, record the conflict rather than choosing the more convenient one. If a figure lacks the denominator or timeframe needed for fair comparison, remove or qualify the numerical claim. The ledger should allow “unresolved” as a state. A deadline can be met by narrowing an article, not by smoothing over a gap.
Use AI for bounded assistance
AI can help identify candidate claims in a draft, suggest search terms for finding original sources, and compare a sentence with an excerpt that the editor provides. Give it the exact paragraph and ask which statements require evidence. Ask it to mark places where a source passage supports only part of a sentence. These tasks are useful because a human can inspect the output against a limited input. Do not let the model turn its own generated citation list into verified evidence. A plausible URL, title, or quotation may be inaccurate or irrelevant. A practical prompt might say: “List the factual claims in this paragraph. For each, mark whether the supplied excerpt supports it, contradicts it, or does not answer it. Quote only short phrases from the excerpt and identify uncertainty.” The editor then opens the actual source and checks the context. If the model says a claim is supported because it found similar words, inspect whether the passage concerns the same population, product version, and condition. The ledger records the human decision, including reasons for changing the sentence. AI can also help with revision after the ledger is stable. Give the writer a set of approved claims and ask for a clearer structure without adding new facts. Compare the revision with the claim list. New numbers, names, broad comparisons, and categorical language should trigger another check. Treat a fluent rewrite as a new draft, not a faithful copy by default. A small review pass after each major restructuring is cheaper than rebuilding provenance at publication time.
Work through one realistic example
Suppose the explainer says, “Automatic captions make every video accessible.” The sentence is too broad. The W3C accessibility material distinguishes captions from other needs such as transcripts and audio description, and quality matters. A better ledger entry records the original sentence, the relevant W3C page, and a narrower takeaway: captions help people access speech and relevant audio information, but an editor still needs to check accuracy and other media alternatives. The article can then explain the practical review step rather than promise universal accessibility. Now suppose a draft says, “Tool X is the best option for small teams.” Even if Tool X's documentation confirms its export format, that source cannot settle the value judgment. The ledger needs the comparison criteria: task, languages, error tolerance, budget, privacy requirements, and an actual test set. If the article has no such test, change the claim to a decision framework. For example: “A small team should compare candidate tools on its own recordings and export needs.” The source record can support the available export feature, while the recommendation is clearly the editor's method. A third case is a date-sensitive product feature. If the article says a feature is free, record the pricing page and check date. But pricing changes; an evergreen explainer may be better served by telling readers which capability and limit to verify before subscribing. The ledger helps make this editorial choice. It turns a fragile assertion into a durable instruction without pretending the website will remain current forever.
Review the ledger before the prose
A final source pass should look at the claim table first. Are there unsupported rows? Does each link open the intended source? Are the date and scope conditions carried into the article? Does the wording distinguish a source fact from an editorial inference? Review high-impact claims with someone who understands the subject. A second person should be able to trace the article's main answer without needing to reconstruct the author's search history. If the source no longer supports the sentence, revise the sentence or stop publication until the point is resolved. After the claim check, read the article as a reader. The ledger is an internal discipline, not the reader's navigation interface. The prose should still answer the question in a clear order, with links where the reader would reasonably want to verify something. A source list at the end can help, but it does not replace in-context attribution for a contested or specific claim. Avoid making the piece feel like a stack of citations; use the ledger to earn concise statements, examples, and honest limits. Record the result of review with simple statuses: supported, narrowed, rewritten, removed, or unresolved. Keep the final sentence linked to its claim ID. When the article changes later, reviewers can see which source rows need reopening. If an editor adds a new example that changes the meaning, it needs its own check. A source ledger is most valuable when it prevents a quick update from reviving an old, unsupported claim.
Make maintenance feasible
A ledger fails if it becomes a second article that nobody can afford to maintain. Choose the smallest table that supports the review: ID, claim, source, passage location, scope/date, decision, and article location. Use ordinary shared documents if that is where the team already works. Assign an owner for the published piece and a trigger for rechecking volatile claims, such as a product version change or a scheduled editorial review. The goal is to make future edits safer, not to create a new content management system. For an evergreen guide, put especially volatile assertions behind a review note. A claim about interface labels or prices will age faster than a method for comparing tools. When updating, change the source entry and the related paragraph together. Check whether the article title, summary, and linked examples still match the revised answer. Version notes help future editors understand why a sentence was narrowed or removed. Without that context, a later rewrite may reintroduce the same attractive but unsupported statement. The payoff is editorial clarity. A source ledger makes it easier to say what the article knows, what it recommends, and what remains uncertain. It lets AI contribute to drafting and checking without asking readers or editors to trust a model's fluent confidence. When the ledger is short, specific, and actively used, the final explainer can be both readable and verifiable.
Check the ledger with a second reader
Before handing the explainer to publication, ask a colleague who did not write it to trace three important claims from the final prose back to the cited passages. Give them the article and ledger, but do not coach them through the search. If they cannot find the supporting sentence, the problem may be the link, the passage note, or the way the claim combines several ideas. Repair that connection while the author still remembers the source. Include one claim that was narrowed during editing and ask whether the final wording preserves the reason for narrowing. A second reader can also spot when the article's strongest takeaway is merely an opinion presented with the visual weight of a sourced fact. Their review does not need to duplicate every research step. It should show that the main argument remains traceable by someone other than its author.
Sources and update note
Follow the linked official source before a product, price, plan, or policy decision.