Abhord Quickstart Guide (Refreshed Edition, January 2026)

This practical guide helps new Abhord users get value fast—from setup to running multi‑LLM surveys, interpreting results, monitoring competitors, and turning insights into action. We’ve included updated practices and new recommendations based on recent platform improvements and shifts in the LLM landscape.

1) Initial setup and configuration

1) Create a workspace

• Add your brand name, website, and primary market/category.
• Invite teammates and assign roles (Viewer, Editor, Admin) to control edit rights for prompts, taxonomies, and exports.

2) Define entities and vocabulary

• Brand terms: official name, product lines, domain variants, ticker, common misspellings.
• Competitors: 5–10 direct and adjacent players, including emerging challengers.
• Category terms: generic product descriptors, use cases, and pain points (e.g., “pricing analytics,” “how to compare X vs Y”).

3) Connect data inputs

• Public web and forums, app/store reviews, owned FAQs/help center, and optionally CSV uploads.
• Set PII-scrubbing on by default; restrict access to sensitive fields via role permissions.

4) Establish survey and analysis defaults

• Region and language coverage; time zone for dashboards and alerts.
• Sentiment model (balanced vs. high‑recall).
• Mention de‑duplication threshold (recommended 85–90% similarity to reduce near‑duplicate noise).

5) Enable governance and audit

• Template your approval workflow for new prompts and taxonomies.
• Turn on versioning so changes to keywords, prompts, and categories are tracked.

New in this edition:

• Smarter entity resolution reduces brand/competitor mix‑ups on look‑alike names.
• Default “balanced” sentiment now accounts for neutral vs. mixed tone more precisely.
• Normalization profiles for major LLMs provide better cross‑model comparability.

2) Run your first survey across LLMs

Goal: Understand how leading LLMs describe your brand, category, and competitors—and what answers users are likely to see.

1) Start a Survey

• Research question: e.g., “What’s the best tool for [use case]?” or “Top alternatives to [Brand]?”
• Audience: Select a panel of top models (at least three) for robustness.
• Prompt set: Include 10–20 real user intents—general (“best X?”), comparative (“X vs Y”), and problem‑based (“how do I…”).

2) Control your variables

• Use a consistent system prompt to standardize tone and content rules.
• Fix temperature to 0–0.3 for determinism; keep max tokens uniform across models.
• Add 1–2 few‑shot examples for complex intents (comparison, pricing, or safety‑sensitive queries).

3) Run and monitor

• Execute in batches to respect rate limits; Abhord auto‑retries transient failures.
• Enable citation capture where available to understand sources shaping answers.

4) Validate quality

• Spot‑check 10% of responses for relevance and hallucinations.
• Flag false positives (misattributed brand mentions) to improve entity resolution over time.

Updated recommendations:

• Use at least one open‑source model in your panel to catch retrieval blind spots.
• Separate “navigational” prompts (“What is [Brand]?”) from “commercial” prompts (“Which tool should I buy?”) for cleaner intent analysis.

3) Interpreting results: mentions, sentiment, share of voice

Key metrics

• Mentions: Count of unique references to your brand