When to use this prompt
When AI engines are recommending competitors but not you, even though your traditional SEO is fine. The most common reason is forum coverage. Reddit threads, Stack Exchange answers, and niche forum discussions are over-indexed in LLM training data and over-cited in retrieval. If you do not show up there, you do not show up in answers.
Run this quarterly. Run it again any time you suspect your “share of mention” in AI engines is decaying.
Expect partial coverage. Reddit restricts automated access, and research tools often cannot see vote counts or post dates, so treat the output as a sample of mentions found, not a census. Spot-check the top results with Reddit’s own search and a few site: searches in Google before you act on them.
The prompt
<role>Research analyst auditing third-party forum and community mentions of a brand for AI search visibility.</role>
<task>Find as many Reddit threads, Stack Exchange answers, Hacker News discussions, Quora answers, and category-specific forum mentions of the brand below as your search tools can reach. Score each by AI-citation potential.</task>
<inputs>
<brand>[BRAND NAME]</brand>
<category>[CATEGORY]</category>
<known_aliases>[ALTERNATE BRAND NAMES, ABBREVIATIONS]</known_aliases>
<competitor_brands>[3-5 NAMED COMPETITORS for benchmarking]</competitor_brands>
</inputs>
<instructions>
1. Search across Reddit, Stack Exchange, Hacker News, Quora, and any 2-3 niche forums relevant to the category. Use live web search; do not infer from training data.
2. For each mention, capture:
- Source platform
- URL
- Thread or post title
- Sentiment: POSITIVE, NEUTRAL, NEGATIVE, or MIXED
- Engagement signal: upvotes, comments, or accepted-answer status if applicable. If you cannot see it, write "could not retrieve".
- Recency: month/year of post, or "could not retrieve"
3. Score each mention on AI-citation potential 1 to 5:
- 5: high engagement, recent (≤12 months), positive or neutral, on a heavily indexed thread
- 1: low engagement, old (>3 years), negative, or on a low-authority subforum
4. Run the same search on the top 2 named competitors. Report the mentions found for each side by side with the target brand for benchmarking, using the same search effort for every brand.
5. Identify 3 specific forum gaps: communities or threads where the category is being actively discussed and the target brand is absent.
6. Do not fabricate threads. If a search returns nothing for a platform, report "No mentions found on [platform]" rather than inventing one.
7. List any coverage gaps you hit: platforms you could not search, pages you could not open, or fields you could not retrieve.
</instructions>
<output_format>
**Target brand mentions (top 15 by citation potential):**
| Source | URL | Title | Sentiment | Engagement | Recency | Score |
|--------|-----|-------|-----------|------------|---------|-------|
**Competitor benchmark:**
| Brand | Reddit | Stack Exchange | HN | Quora | Niche Forums | Total found |
|-------|--------|----------------|-----|-------|--------------|-------|
**Top 3 forum gaps:**
1. [Community or thread]: [why it matters]
2. ...
3. ...
**Top 3 mentions to amplify:**
For positive mentions with high citation potential, name the 3 worth supporting (linking to from your own content, sharing further, or thanking the author). Do not suggest astroturfing.
**Coverage gaps:** [platforms, pages, or fields that could not be retrieved]
</output_format>How it works
Reddit and other community sources are well represented in LLM training data because they hold large volumes of candid, question-and-answer dialogue. When an AI engine searches the web at answer time, those same threads often appear again because they continue to rank well in classic search. The result: a brand’s presence (or absence) in forums is one of the strongest signals of AI-citation eligibility.
The 1-5 scoring on AI-citation potential prevents the audit from becoming a list. Engagement and recency together predict whether a thread will actually surface. A 2018 Reddit post with 4 upvotes is not the same input as a 2026 thread with 800 upvotes and a top-voted answer.
The competitor benchmark column is the part that creates urgency. Most teams underestimate how many forum mentions their competitors have until they see the numbers next to their own. Because no tool sees every thread, the counts are “mentions found” with equal search effort per brand; read them as relative, not absolute.
The coverage-gaps field keeps the audit honest. Reddit limits automated access, and many research tools cannot see vote counts or dates. Asking the model to name what it could not reach stops it from quietly filling those blanks.
The “do not suggest astroturfing” line in the output format is intentional. Frontier models will sometimes propose growth-hacky tactics. The explicit boundary keeps the recommendations clean.
Example output
Target brand: 14 mentions found across surfaces. Top 3 by citation potential:
- r/SaaS post: “Best product analytics for early-stage”: 240 upvotes, 2026-03, POSITIVE, score 5/5
- Hacker News, brand featured in launch thread: 180 points, 2026-01, NEUTRAL, score 4/5
- Stack Overflow answer mentioning brand SDK: 12 upvotes, accepted answer, 2025-11, POSITIVE, score 4/5
Competitor benchmark: Target: 14 mentions found. Competitor A: 89 found. Competitor B: 47 found.
Top 3 forum gaps:
- r/dataengineering: active category discussions, 0 brand mentions
- The Indie Hackers community: heavy SaaS discussion, 0 mentions
- Hacker News “Ask HN: best tool for X” recurring threads: competitors named, target absent
Variations
- Geographic version: Restrict the search to a specific region (e.g., r/CanadianSaaS, regional Stack Exchange sites) for region-targeted brands.
- Topic-specific version: Search for category questions where the brand is not mentioned, surfacing the highest-leverage threads to engage authentically.
- Time-series version: Run quarterly and track mention counts over time to detect community-presence decay.