How to do an AI content gap analysis, step by step

Classic content gap analysis asks: what keywords do ranking pages cover that mine doesn't? That question is aging badly. When the answer users see is a constructed AI response, the gap that matters is between your article and the answer — its sub-questions, its claims, its evidence standards. Here's the full workflow, then a complete worked example through every tab.

Before you start: pick targets that can pay

• The keyword triggers an AI Overview today (check by searching it — no Overview, no citation to win).
• You have a genuine article for it — this workflow improves real pages; it doesn't conjure them.
• The query has commercial gravity — informational-commercial hybrids ("best…", "how much…", "how long…") are where citations convert to pipeline. If traffic is already bleeding, start from the traffic-drop diagnostic to pick keywords.

The seven-step workflow

1. Enter URL + keyword. Open LLM Gap Analyzer → Google AI Gap Analyzer tab → paste your article URL and its target keyword → Analyze. The tool captures the live AI Overview at that moment (2–3 minutes). Note: re-running the same pair in the same month costs a second search, so log results as you go.
2. Read the Google AI Answer tab. The full answer, segmented by source — click "View all sources" under any segment. First question: are you cited at all? Yes → defense audit; no → offense audit.
3. Read the Answer Breakdown. Six layers: Query Understanding, Source Analysis, Content Selection, Deconstructed Reasoning, Source-Driven Framing, Construction Actions. Theory behind them: how AI Overviews choose sources.
4. Grade the claims. Claim Analysis → Overall Assessment names the answer's biggest gap and most vulnerable claim; Weak & Unsourced claims are your target list.
5. Read Content Gaps Found. Every sub-topic, fact, and element the cited sources have that your article lacks, in the context of this specific answer.
6. Ship Article Improvements. Expert Note + exact Location + ready-to-paste Suggested Replacement Content + which competitor article you'll replace. Edit, publish, request indexing.
7. Re-run after recrawl. Same URL + keyword, one to two weeks later. Cited now? Log the win. Not yet? The new Claim Analysis shows what the AI still prefers — iterate on evidence, not guesses.

Full worked example: "employee onboarding software"

An HR-tech vendor's comparison page ranks #6 for employee onboarding software; the Overview cites three other sources. Here's the complete analysis, tab by tab. (Sample analysis — run your own URL + keyword for the live version.)

Tab 1 · Google AI Answer: four segments — what onboarding software does · key features to look for · typical pricing · implementation time. Cited: an HR blog (segments 1–2), a review aggregator (segment 3), a competitor's guide (segment 4). Your page: not cited. Offense audit it is.

Tab 2 · Answer Breakdown: Query Understanding expanded the keyword into "what is it / which features matter / what does it cost / how long to roll out / how it differs from HRIS." Your page never addresses the HRIS distinction — one segment forfeited before the audition began. Source-Driven Framing: the HR blog set the answer's tone. Content Selection favored bounded vocabulary: "30-60-90," "e-signature," "$4–8 per employee," "two to four weeks."

Tab 3 · Claim Analysis:

Tab 4 · Content Gaps Found (your article vs. the cited sources): ① no onboarding-vs-HRIS section (forfeited segment); ② pricing listed without dates or per-tier breakdown; ③ no implementation-timeline data despite you having 200+ real customer rollouts; ④ no background-check integration matrix.

Tab 5 · Article Improvements (shipped):

Tab 6 → re-run, two weeks later: cited for the pricing and implementation segments; the HRIS section entered the answer's structure. That's the loop: gaps → passages → citations, with first-party data (the 214-rollout figure) doing the heaviest lifting — it's the one passage no competitor can copy.

Turning one analysis into a system

The base plan's 30 monthly searches map onto a repeatable operation: ~10 new-keyword audits, ~10 re-runs verifying last month's fixes, ~10 held for defense (re-checking keywords you already win — citations decay as competitors freshen up). Track everything in one sheet: keyword → date → cited? → weakest claim → fix shipped → result. Within a quarter you'll have the one document most content teams still don't: a ledger of which edits flip citations in your niche.

Advanced: the flanking audit (one URL, many doors)

The standard workflow analyzes your URL against its head keyword. The advanced move exploits query fan-out: the head answer is assembled from sub-queries, and each sub-query is a separate audition you can enter. The flanking audit:

1. Read Query Understanding for the head keyword. List every sub-question the AI expanded into — that's the answer's real table of contents.
2. Run your URL against 2–3 sub-questions as their own keywords. ("employee onboarding software" → "onboarding software implementation time", "onboarding vs HRIS"). Each run shows whether a dedicated passage could win that segment.
3. Ship one passage per winnable sub-query. Self-contained, dated, evidenced — the grounding-magnet structure from the claim study.
4. Re-run the head keyword. Flanking passages routinely enter the head answer's segments without any change in the head-term ranking. You outflanked the incumbent instead of outranking them.

The citation ledger (the exact columns)

The teams that compound at this don't keep notes — they keep a ledger. Columns that have earned their place: keyword · date run · Overview present? · cited (you / competitor / UGC)? · framing source · most vulnerable claim (verbatim) · claim type (figure / absolute / sample-of-one / circular) · fix shipped (URL + date) · recrawl date · re-run result · CTR before/after (GSC). Two quarters of this and you own something no agency deck contains: empirical flip-rates by claim type for your niche — which converts next quarter's content plan from opinion to arithmetic.

The arithmetic, worked: 30 searches/month ≈ 10 new audits + 10 verification re-runs + 10 defense checks. If your ledger shows (say) decayed-figure slots flipping at 60% and absolutes at 40%, ten targeted edits a month is 4–6 new citations — each worth ~35% CTR lift on its query. That's the compounding loop, measured.

Go deeper: what to write in each passage → the E-E-A-T repair checklist. What you're likely to find in the answers → the study. When traffic recovery is the driving goal → the recovery diagnostic.

Frequently asked questions

Dmitry Dragilev

4× acquired founder: Polar Polls → Google (2014), JustReachOut → SEOJet (2020), Smallbiz.Tools → Semrush (2023), SERP Gap Analyzer — the #1 top-performing app in the Semrush App Center → Semrush (2025). Contributor to Forbes, TechCrunch, WIRED, and Moz since 2009. More at criminallyprolific.com.