Waikay Topic Report Guide

27 August, 2025

You’re about to ask four language models a simple, powerful question: “What do you know about our brand in the context of TOPIC?” Because these models are constrained to what they already know about your brand—not the open web—the topic you choose acts like the lens for everything the report will reveal. Pick it well, and you’ll see exactly where your brand stands and what to improve.

What the topic report does

A topic report asks a very specific question of four independent large language models: “What do you know about [your brand] in the context of [entity]?” It does the same for your two closest competitors, giving you a side‑by‑side view of how each brand is represented within the models’ existing knowledge.

Because this process works only with what the models already know — without live web browsing — the results are a clean reflection of your brand’s foundational presence inside these systems. This is where true long‑term visibility starts: the stronger and more consistent your brand’s association with an entity, the more likely it is to appear naturally across a wide range of prompts.

The report distils these findings into a measure of semantic strength — how confidently and consistently each model links your brand to that entity. It then surfaces specific opportunities to reinforce weak or missing associations. Acting on these recommendations strengthens your brand’s footing in the models’ memory, which in turn can lift performance across multiple related prompts from the top down, without having to chase every single query variation individually.

What a topic is

• Definition: A topic is one discrete concept or entity that exists in your business context.
• Examples: For a dog food brand, relevant topics might include “chicken,” “beef,” and “fish.” Each is its own topic.
• Why it matters: LLMs encode brand–topic associations. Strong associations mean you’re more likely to be surfaced, remembered, and described accurately in that context.
• Optimization path: You strengthen a topic by aligning content, products, and messaging around that single concept over time.

Two reliable ways to choose your topic

When it comes to deciding which topic to run next, there are two proven approaches you can use. Each gives you a clear, data‑driven path — the difference is simply whether you start by protecting what you already have, or by building out your foundational coverage from scratch.

Prompt data first

• Use case: You want to shore up weak spots and catch slippage early.
• What to do: Review how often each topic is returned over time in Waikay’s tracking.
• Decision rule: If a once-strong entity is fading or a strategic entity is underrepresented, prioritize that topic for your next report.
• Benefit: You act on real drift and gaps, not guesses, tightening your brand’s semantic coverage where it’s needed most.

Core entities first

• Use case: You want baseline coverage across your foundation.
• What to do: List your core entities—the primary products, services, ingredients, use cases, or audiences you must be known for.
• Decision rule: Create one report per core entity, one topic at a time.
• Benefit: Ensures complete, systematic coverage of the concepts that define your business outcomes.

How to phrase your topic

• One thing: Choose a single, unambiguous noun or short noun phrase.
• No long-tails: Avoid queries, qualifiers, or “best-of” language. That’s a prompt, not a topic.
• Brand relevance: Use concepts that you sell, publish on, or want to be known for—not tangential terms.
• Disambiguation only when needed: If a word has multiple senses, add the minimum qualifier that keeps it one concept (e.g., “turkey dog food” vs. “turkey”).

A simple workflow to pick your first five topics

1. List core entities: Write down 5–10 must-win concepts across products, ingredients, audiences, and use cases.
2. Check current coverage: Scan Waikay trend data to spot weak or declining entities you can quickly shore up.
3. Select the first five: Mix 3 core entities with 2 weak-but-strategic entities to balance foundation and upside.
4. Phrase cleanly: Reduce each to one concept. Strip qualifiers, intents, and adjectives unless needed for disambiguation.
5. Run and compare: Generate reports for all five and compare cross-model consistency to prioritize the next actions.

Quality checklist before you run the report

• Singularity: Is this exactly one concept?
• Relevance: Is this central to how we want to be known?
• Clarity: Would five different people interpret it the same way?
• Coverage intent: Does this fill a gap or reinforce a core?
• Scalability: Can we repeat this pattern across our entity set?

Common mistakes to avoid

• Mushing concepts: Combining ingredient, audience, and benefit into one phrase.
• Using queries: Writing search-like prompts instead of entities.
• Over-qualifying: Adding adjectives that create micro-niches with thin representation.
• Going off-brand: Picking trendy concepts that your brand doesn’t actually support.
• Inconsistency: Using different terms for the same concept across reports.

What to do after you choose the topic

Once you’ve chosen a topic, running a topic report is like taking a snapshot of how four independent AI models currently “think” about your brand in that specific context. This instantly gives you a competitive benchmark, showing not just your own positioning but how it stacks up against key rivals.

The model‑to‑model comparison highlights both consensus and outliers — consensus shows you where your brand is already embedded in the AI’s understanding, while outliers flag the gaps or inconsistencies to fix. Those findings translate directly into actionable briefs for your content, product pages, and PR, giving you a clear plan to strengthen that brand–entity link.

By re‑running the topic report at regular intervals, you can track the lift from your optimisation work, catch any early signs of drift, and keep reinforcing the association. The result is a stronger foundation in model memory — one that can boost performance across multiple related prompts, not just the single question you started with.

Genie Jones

Genie Jones is a Knowledge Graph Manager at InLinks and Waikay. A Warwick University graduate with a degree in Language, Culture, and Communications, she combines her passion for linguistics with website optimization. Genie specializes in using linguistic insights to enhance content structure, improve SEO, and manage knowledge graphs, helping brands connect effectively with their audiences.