Run the alphabet method against a keyword and you’ll get a long list back — and if you’ve done this before, you already know some of it will be junk. Not “low quality” junk, but genuinely unrelated junk: suggestions that have nothing to do with your seed keyword at all, sitting right alongside the useful long-tail phrases you were hoping for. Understanding why that happens, and how to filter it out, is what makes the A-Z Suggest method usable instead of a chore.
Why Irrelevant Suggestions Show Up at All
The alphabet method works by appending each letter to your seed keyword and asking Google’s autocomplete what it would suggest for that combination. Most of the time, that produces a real, relevant completion. But not every seed+letter pairing has a strong match in actual search behavior — sometimes there simply aren’t enough people searching for “your seed keyword” plus something starting with, say, “q” or “x” for Google to have a confident, on-topic suggestion ready.
When that happens, autocomplete doesn’t just return nothing — it falls back to something else, typically pulling from unrelated trending searches or spelling-correction suggestions that have no real connection to your seed term. Those fallback suggestions look identical in format to the genuine ones; there’s no flag or label distinguishing them. Left in your list unfiltered, they quietly dilute the batch with noise that has nothing to do with your topic — and if you’re skimming quickly, it’s easy to waste time evaluating a suggestion that was never actually about your keyword in the first place.
The Filtering Rule That Fixes Most of It
The fix that removes the vast majority of this noise is simpler than it sounds: only keep suggestions where every word from your original seed keyword actually appears somewhere in the returned phrase. A genuine long-tail expansion of your seed will almost always retain the seed’s own words, just with something added — a question word, a modifier, a related term. A fallback suggestion that Google generated because it had nothing better to offer typically doesn’t share the seed’s vocabulary at all, because it was never actually related to begin with.
This rule is deliberately simple rather than clever, and that’s the point — a simple, consistent filter is something you can trust to run every time, rather than a fuzzy judgment call you have to remake by hand for every batch of results.
What Gets Filtered Out (and Why That's Correct)
Applying this rule will drop a real percentage of your raw suggestion list — sometimes a substantial one, depending on how niche or low-volume your seed keyword is. That’s expected and correct, not a sign the tool is being overly aggressive. A seed keyword with less search volume around it produces more empty seed+letter combinations, which means more fallback noise in the raw output, which means a bigger cut once the filter runs. The size of the cut is really just a signal about how much genuine search interest exists around your topic in the first place.
What the Filter Won't Catch
It’s worth knowing the limits of a word-matching filter so you don’t over-trust it. It will let through suggestions that technically contain every seed word but are still low-value — for instance, an overly generic variation that doesn’t represent a distinct search intent from your seed keyword itself. It also won’t catch a suggestion that’s topically relevant but happens to phrase itself without directly repeating your exact seed words (a genuine synonym-based variation, for example). The filter is a blunt instrument that removes the obviously unrelated noise — it’s not a substitute for a final human skim of the surviving list before you commit to writing any of it.
A Practical Two-Pass Process
- Run the A-Z expansion and let the seed-word filter do its automatic first pass — this alone removes most of the off-topic noise.
- Skim the surviving list for anything that technically passed the filter but still reads as low-value or too generic to represent a distinct piece of content.
- Group what’s left by apparent search intent, since a filtered list is usually small enough to actually read in full rather than just skim.
- Cross-reference the strongest survivors against your competitor gap report before writing, to confirm they’re genuinely untapped.
Why This Was Worth Fixing
Before this kind of filtering, the A-Z method’s biggest practical complaint wasn’t that it produced too few ideas — it was that a meaningful chunk of what it produced was actively irrelevant, which made the output feel untrustworthy even when the genuine suggestions buried inside it were good. A tool that returns a shorter, cleaner list is more useful than one that returns a longer list you have to second-guess. Filtering to require every seed word’s presence doesn’t just clean up the output cosmetically — it restores confidence that what survives is worth your actual writing time.
