Product Guides1 min read • Feb 10, 2026By Jordan Reyes

How to interpret AI sentiment scores for your brand (Feb 2026 Update 2)

Abhord Product Guide (2026 Refresh): From Setup to Action

Abhord Product Guide (2026 Refresh): From Setup to Action

What’s new in this edition

  • Broader LLM coverage and modes: treat “chat” vs. “search-augmented” answers as separate panels when you can. Results often diverge meaningfully by mode.
  • More robust prompt sets: include neutral category, comparative, and “reasons why” prompts to capture both awareness and consideration signals.
  • New diagnostic KPIs to track alongside mentions: citation rate (links to your domain), first-mention prominence, and answer consistency across runs.
  • Stronger governance: standardize aliases and disambiguation early—this improves match quality and cuts false positives downstream.

1) Initial setup and configuration

Your goal in setup is clean entity definition and reproducible runs.

  • Create a workspace and project

- Name by market and theme (e.g., “US – Project Management – Q1 2026”).

- Select target locales and languages you plan to test. Keep each project single-locale for cleaner comparisons.

  • Define entities

- Brand: official name, website, and canonical description.

- Aliases: product lines, abbreviations, prior names, misspellings. Include competitor aliases now; it reduces later clean-up.

- Disambiguation

Jordan Reyes

Principal SEO Scientist

Jordan Reyes is a 15-year SEO and AI search veteran focused on search experimentation, SERP quality, and LLM recommendation signals.

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