Abhord Quickstart Guide (2026 Refresh)

This practical, refreshed guide helps new Abhord users go from zero to actionable insights across generative engines. It reflects February 2026 updates: expanded model coverage, a unified Survey Builder, context‑normalized sentiment, quality‑adjusted share of voice, and automated competitor tracking.

1) Initial setup and configuration

1) Create your workspace

• Sign in, create an Organization, and invite teammates with roles: Admin (billing + model keys), Analyst (surveys + dashboards), Viewer (read‑only).
• Set your default locale and vertical (e.g., SaaS, e‑commerce) to load relevant entity taxonomies and templates.

2) Connect models

• Options:

- Abhord‑managed endpoints (recommended to start): OpenAI, Anthropic, Google, Meta, Mistral, and Cohere with pre‑tuned safety + rate limits.

- BYO keys: add provider keys per workspace; set per‑model daily and per‑survey budget caps.

• New: model health panel shows latency, failure rate, and effective tokens/$ to guide selection.

3) Data governance

• Enable Privacy Mode (on by default): no prompts/outputs enter model training; PII scrubbing for emails, phone numbers, locations.
• Configure retention (30/90/365 days) and redact rules for brand‑sensitive terms.

4) Taxonomy and entities

• Add your brand, products, and competitors with canonical names and synonyms (e.g., “Acme X1,” “X‑1,” project codenames). This boosts accurate mentions and avoids double‑counting.
• Define categories (pricing, reliability, support, ethics, sustainability) to tag responses automatically.

5) Integrations

• Slack/Teams alerts, Jira/Asana for action items, BigQuery/Snowflake export, and Webhooks for downstream analytics.
• New: Answer Snapshot link embeds the latest cross‑LLM consensus in Confluence/Notion.

Pro tips

• Lock a “baseline prompt” project to benchmark future drift.
• Keep a consistent temperature/top‑p across engines for fair comparisons.

2) Run your first survey across LLMs

Goal: assess how major LLMs answer a consumer query about your product versus a competitor.

1) Open Survey Builder (unified in 2026)

• Choose a template (e.g., “Comparative Product Lookup,” “Best‑for Recommendation,” or “Pricing & Plans Clarifier”).
• Set objective and primary KPIs (mentions, sentiment, share of voice, hallucination rate).

2) Draft questions

• Example seed: “Which project management tool is best for small remote teams? Consider features, pricing, and integrations.”
• Add 3–5 variants to reduce prompt‑specific bias. Use Variables for brand/product slots.

3) Select engines and geos

• Pick at least 4 engines for coverage (e.g., OpenAI GPT‑4.1‑mini, Anthropic Claude 3.7 Sonnet, Google Gemini 2.0 Flash, Meta Llama 3.2 70B).
• Optional: run localized queries (en‑US, en‑GB, de‑DE) to detect regional drift.

4) Controls and sampling

• Temperature 0.2–0.4, max tokens 512–768, deterministic STOP sequences.
• Sample size: minimum 30 outputs per engine per variant for stable metrics.
• Enable “Randomized Ordering” so no engine consistently sees the same variant first.

5) Cost and safety

• Use the built‑in cost estimator; set a hard budget ceiling.
• Turn on Safety & Hallucination Gates: block obviously off‑topic or unsafe results; flag answers with external claims lacking sources.

6) Launch and monitor

• Live console shows engine latency and completion rates.
• If one model degrades, Auto‑Retry kicks in; you’ll see a health badge and optional pause.

What’s new since last edition

• Unified Survey Builder with variable injection and multi‑geo scheduling.
• Automatic debiasing: prompt rotation + consensus aggregation.
• Cost‑aware routing and engine health scoring.

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

Mentions

• Definition: count of entity references (exact + synonyms) in each answer, deduped per response.
• Best practice: review the Synonym Map to avoid misattribution (e.g., “Asana” vs “asana” the yoga term).
• Metrics: total mentions, mentions per 100 responses, and visibility in top‑3 recommendations.

Sentiment

• Abhord’s sentiment v2.6 blends polarity, intensity, and context labels (value, reliability, UX, ethics).
• New: context‑normalized sentiment adjusts for answer length and hedging