Documentation Index
Fetch the complete documentation index at: https://docs.haloxlabs.ai/llms.txt
Use this file to discover all available pages before exploring further.
Fan-out search patterns
AI search engines do not answer a user question only once in a straight line. ChatGPT, Gemini, Perplexity, Claude, and other answer engines often expand a question into sub-questions, comparison criteria, source candidates, and validation loops. HaloX treats this as a fan-out search pattern. For example, “Which GEO tool should our industry use?” can expand into:- What GEO means
- Which use cases exist in this industry
- How GEO differs from SEO tools
- What to check before adoption
- Which sources, reviews, newsrooms, or guides are trustworthy
One-line definition
Fan-out is the path from root question → sub-questions → source candidates → validation loop → final answer/citation.
| Stage | What AI systems do | What to do in HaloX |
|---|---|---|
| Root question | Interpret the original user question. | Add representative questions to a strategic prompt set. |
| Sub-questions | Split into definition, comparison, buying criteria, location, risk, and review questions. | Use Strategy Map to inspect clusters and gap types. |
| Source candidates | Look for evidence across official sites, newsrooms, blogs, reviews, video, communities, and articles. | Use Citation Tracking to separate Source from Citation. |
| Validation loop | Compare consistency, authority, freshness, and structure across sources. | Use Site Audit and Content Factory trust reports. |
| Answer/citation | Mention a brand or cite a link in the final answer. | Track citation rate, question share, and repeated competitor exposure in reports. |
Why this came up in meetings
Customer and partner meetings kept repeating the same point: a single brand page is not enough. You need to follow the sub-questions and source paths that AI systems use.| Meeting requirement | Fan-out interpretation | Connected HaloX features |
|---|---|---|
| Financial/platform teams want non-brand conversion questions, not only brand search. | “Before signing up,” “comparison,” “how to,” and “best” questions fan out from the root intent. | Prompt Analysis, Strategy Map, Content Factory |
| Enterprise communications teams want leadership, issue, newsroom, and owned-media monitoring. | Brand-definition questions fan out into leadership, issues, owned media, and external articles. | Citation Tracking, Site Audit, Weekly Reports |
| PR/brand teams ask about Naver, Google, and AI-channel differences. | The same question can lead to Naver blogs, newsrooms, YouTube, articles, LinkedIn, and other sources. | Citation Tracking, Agency GEO operations |
| B2B SaaS teams ask about GEO after SEO drops. | Recover the core search topic first, then expand into problem, comparison, and adoption questions. | Site Audit, Strategy Map, Content Factory |
| Local businesses ask about local GEO. | Questions split into “location + recommendation,” service comparison, before-visit questions, and English/global questions. | Local GEO guide, Prompt Analysis |
How to analyze fan-out in HaloX
1. Split one representative question into question groups
Separate brand, non-brand problem, comparison, buying/adoption, location/language, and issue questions. Explain prompts as “core questions AI receives.”
2. Inspect sub-question clusters in Strategy Map
Fan-out questions usually spread across several keyword clusters. Review
search volume, AIO, GEO, SEO, gap type, urgent, and GEO opportunity together.3. Check source paths with Citation Tracking
Separate whether your brand is only mentioned, used as a source candidate, or explicitly cited as a link. Repeated competitor citations are high-priority content gaps.
4. Verify whether AI systems can read the content
Even good content may fail if robots rules, CDN behavior, schema gaps, JavaScript dependence, or performance issues block discovery.
Example fan-out prompt sets
| Industry / objective | Root question | Fan-out sub-questions | Needed content |
|---|---|---|---|
| Financial platform | “Best crypto exchange” | How to trade, fee comparison, security, before-signup checks, app usability | Comparison page, beginner guide, FAQ, security/policy page |
| B2B SaaS | “Best CRM marketing automation tool” | Definition, adoption criteria, alternatives, case studies, pricing/operations | Problem guide, comparison content, checklist, case page |
| PR/brand | “What kind of company is this?” | Leadership, issues, business structure, official newsroom, external articles | Fact sheet, newsroom hub, FAQ, issue explainer |
| Local business | “Best clinic in Gangnam” | Location, service criteria, before-visit questions, reviews/trust, foreign-language support | Branch landing page, local FAQ, service comparison, English guide |
| Agency proposal | “How do you diagnose GEO?” | Diagnosis items, score interpretation, execution priority, report outputs | One-page diagnosis, operating loop, report sample |
Why citation reason matters
A brand appearing in an AI answer is not enough. The role inside the answer matters.| State | Plain-language explanation | Next action |
|---|---|---|
| Mention | “The name appears, but we do not know whether it was used as evidence.” | Check brand accuracy and competitor co-mentions. |
| Source | “The page became an evidence candidate.” | Strengthen structure, references, freshness, and internal links. |
| Citation | “The page appeared as a cited link/source.” | Track whether it repeats across the same question group. |
| Competitor citation | “A competitor was selected as evidence.” | Compare their page format with your content gap. |
| No reliable source | “AI relies on generic summaries instead of official evidence.” | Add FAQ, fact sheets, entity hubs, schema, and bot access. |
Convert questions into content
| Content element | Why it matters | Example |
|---|---|---|
| First-paragraph definition | Gives AI a clear answer sentence. | “GEO is an operating method for managing brand mentions, sources, and citations in AI answers.” |
| Question-led H2/H3 | Matches the sub-query shape. | “Why is my AI citation rate low?” |
| Comparison table | Supports recommendation and comparison questions. | Criteria table across options. |
| FAQ | Captures real customer objections. | “Should we do GEO before SEO is fixed?” |
| Evidence/source links | Increases citation readiness. | Official docs, newsroom, data, policy, examples. |
| Internal links | Connects the fan-out path. | Definition → comparison → playbook → report. |
Talk track for customers
“AI does not answer a question as a single keyword lookup. It expands the question into related sub-questions and source checks. HaloX therefore monitors prompt sets, clusters, sources, citations, and content actions as one operating loop.”