Content and AI Search

A. Design your ontology and taxonomy

What

Map the domain of your brand:

• Core themes and use cases.

• Entities (products, services, locations, technologies, personas, problems).

• Relationships (is-a, part-of, used-for, works-with, vs., etc.).

Why

A clear ontology: 

• Prevents random, unstructured content production.

• Ensures that every piece fits into a knowledge graph-like view of your site.

• Helps AI systems see you as the reference domain for specific topics.

 Action

• Build a simple ontology document:

  • Example (mini painting): Techniques → OSL, NMM, Weathering; Media → acrylics, oils; Factions → Rebel Pathfinders, Clone Troopers; etc.

• Translate ontology into taxonomies and content hubs:

  • Pillar pages for broad themes (e.g., “Advanced Miniature Painting”, “Enterprise Learning Solutions”).

  • Cluster pages for specific entities or subtopics.

• Ensure internal linking mirrors this structure:

  • Pillar ↔ clusters; clusters ↔ related clusters; hubs ↔ commercial/transactional pages.

B. Write for retrieval: chunks, context windows, and HyDE-friendly structure

What

Content must be chunk-friendly for dense retrieval and RAG:

• Short, self-contained paragraphs that each answer a micro-question.

• Clear headings aligned with intents and entities.

• Explicit question-answer patterns (“What is…?”, “How to…”, “Pros and cons…”).

Why

• RAG and answer engines slice content into chunks (e.g., 256–512 tokens).

• Techniques like HyDE (hallucinating an answer to retrieve better documents) work best when real content looks like a good answer.

• When question and answer sit together in the same chunk, it is easier for ChatGPT, Gemini, Claude, Perplexity, etc., to select that passage.

Action

• Apply the Inverted Pyramid at both page and section level:

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  • Lead with the definition, key fact, or primary answer. Elaborate later.

• Create micro-Q&A sections:

  • Use H2/H3 “What is…?”, “How does… work?”, “Is… safe?”, “When should I choose…?”

  • Provide 2–4 sentence direct answers before expanding.

• Standardise semantic chunks:

  • Each paragraph covers one idea and can stand alone if quoted.

  • Avoid long, meandering sections that mix multiple concepts.

C. Write for reasoning: CoT-compatible content

What

Reasoning-heavy tasks (diagnostics, strategy, comparison, planning) are handled by models using techniques like Chain of Thought, Tree of Thoughts, or ReAct.

LLMs need sources that encode reasoning patterns, not just facts.

 Why

When ChatGPT, Gemini, Claude, or Perplexity explain:

• “Which loan option is best for this case?”

• “How to design an AI-ready content hub?”

• “What’s the best painting workflow for a display miniature?”

they look for content that:

• Walks through problem → analysis → method → solution.

• Uses numbered steps, explicit trade-offs, and cause-and-effect relationships.

• Offers worked examples and edge cases.

This kind of structure is extremely valuable as a “reasoning scaffold” for the model.

Action

• Use the Reasoning Trace pattern:

  • Problem → Diagnosis (what matters) → Options → Recommended path → Caveats.

• Add step-by-step sequences:

  • For how-to or workflows, always provide numbered lists with clear sub-steps.

• Include comparisons and decision frameworks:

  • Tables and bullet lists that contrast options (“when to use A vs. B”) are frequently reused by answer engines.