Reddit and Forum Citation Finder

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.

The prompt

<role>Research analyst auditing third-party forum and community mentions of a brand for AI search visibility.</role>

<task>Find every Reddit thread, Stack Exchange answer, Hacker News discussion, Quora answer, and category-specific forum mention of the brand below. 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
   - Recency: month/year of post
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 their counts side by side with the target brand for benchmarking.
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.
</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 |
|-------|--------|----------------|-----|-------|--------------|-------|

**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.
</output_format>

How it works

LLM training data is heavy on Reddit and other community sources because that content is uniquely high-quality dialogue and is heavily linked. When an AI engine retrieves at inference time, those same sources 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.

The “do not suggest astroturfing” line in step 4 is intentional. Frontier models will sometimes propose growth-hacky tactics. The explicit boundary keeps the recommendations clean.

Example output

Target brand: 14 total mentions across surfaces. Top 3 by citation potential:

1. r/SaaS post: “Best product analytics for early-stage” — 240 upvotes, 2026-03, POSITIVE, score 5/5
2. Hacker News: brand featured in launch thread — 180 points, 2026-01, NEUTRAL, score 4/5
3. Stack Overflow answer mentioning brand SDK — 12 upvotes, accepted answer, 2025-11, POSITIVE, score 4/5

Competitor benchmark: Target: 14 mentions. Competitor A: 89 mentions. Competitor B: 47 mentions.

Top 3 forum gaps:

1. r/dataengineering — active category discussions, 0 brand mentions
2. The Indie Hackers community — heavy SaaS discussion, 0 mentions
3. 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.