Abhord Quickstart Guide (Refreshed – March 2026)
Use this practical, step‑by‑step guide to get value from Abhord in your first week. It reflects product improvements and fresh recommendations for running cross‑LLM surveys and turning insights into action.
What’s new in this edition
- Faster setup: an Onboarding Wizard creates your first Workspace, Project, and Entities in under 5 minutes.
- Multi‑LLM routing: balanced sampling and randomized prompt variants reduce single‑model bias by default.
- Mention clustering: smarter deduplication groups near‑identical answers while preserving outliers.
- Aspect sentiment: sentiment is now available overall and by aspect (price, quality, support, safety).
- Visibility‑weighted Share of Voice (SOV): weights mentions by answer prominence and consistency across models.
- Guardrails: optional hallucination screening and reference‑checking flags low‑confidence mentions.
- Playbooks: ready‑made “Act on Insights” templates for Support, Product, and SEO teams.
1) Initial setup and configuration
Goal: stand up a clean project that captures your brand and competitor mentions reliably.
- Create a Workspace and Project
1. Sign in and launch the Onboarding Wizard.
2. Name your Workspace (e.g., “North America”) and your first Project (e.g., “Consumer Laptops Q2”).
3. Choose default language/locale and time zone.
- Define Entities (critical)
1. Add your brand and product entities. Include:
- Official names, common nicknames, and frequent misspellings.
- Key SKUs/models and relevant category terms.
2. Add exclusion terms to avoid false positives (e.g., “Apple” ≠ fruit recipes).
3. Toggle PII redaction on (recommended) to minimize sensitive data exposure in free‑text answers.
- Connect LLM sources
1. Select at least three model families for balance (e.g., GPT‑class, Claude‑class, Gemini‑class).
2. Accept default balanced sampling unless you have a contractual volume requirement.
3. If you bring your own API keys, set per‑provider rate limits and budgets.
- Set governance
1. Roles: assign Admin (setup), Analyst (surveys), Viewer (stakeholders).
2. Compliance: enable audit logs and export controls if your org requires them.
Pro tip: Keep entity synonyms tight at first. It’s easier to expand than to untangle noisy matches later.
2) Run your first survey across LLMs
Goal: gather consistent, comparable answers from multiple models to the same real‑world questions.
- Start with a proven question set
1. Use a template like “Top recommendations,” “Alternatives to…,” and “Best for X use case.”
2. Include at least one “Why?” or “Pros/Cons?” prompt to surface reasoning signals.
- Configure prompts
1. Use Abhord’s Prompt Variant generator (A/B/C wording) to reduce prompt‑specific bias.
2. Set answer format expectations (bullets, short paragraphs) for easier parsing.
3. Add disambiguation instructions for ambiguous brand names.
- Sampling plan (new recommendation)
- Minimum: 150–300 responses per model per key question.
- If your category is long‑tail, target 500+ per model to stabilize SOV.
- Enable randomized order of brands to avoid position bias.
- Quality guardrails
1. Turn on hallucination screening and citation checking if available.
2. Enable answer deduplication (clustering) at “Standard” to start; increase to “Aggressive” if you see obvious repeats.
- Launch and monitor
1. Run a 10% pilot to confirm entity capture and sentiment look sane.
2. Scale to full run; watch cost and completion rates by model.
3) Interpreting results: mentions, sentiment, share of voice
- Mentions
- What it is: count of times your entities appear in qualified answers.
- Practical read: Look at Net Mentions = Mentions – Exclusions – Low‑confidence flags.
- Check clustering: If clustered mentions >60%, you may have over‑similar prompts; add a variant.
- Sentiment
- What it is: normalized 0–100 score overall and by aspect (price, quality, support, safety).
- Practical read:
- 0–39: negative skew; 40–59: mixed/neutral; 60–79: positive; 80–100: advocacy.
- New tip: Track Aspect Lift = Aspect Sentiment – Overall Sentiment. Positive lift shows what to amplify; negative lift shows where to fix.
- Share of Voice (SOV)
- What it is: percentage of total qualified, visibility‑weighted mentions captured by an entity.
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