LLM Citation Tracking: 4 Step Implementation Guide

Measuring your brand’s presence inside AI assistants without a standardized protocol produces noisy, non-actionable data—this 4-Step LLM Citation Tracking Implementation Guide turns vague “are we mentioned?” checks into an auditable measurement system that any team can run weekly. It supplies a calibrated prompt bank, a 0–13 scoring rubric (frequency, sentiment, accuracy), evidence logging templates, and reporting views so you can baseline current visibility, benchmark against competitors, and turn gaps into prioritized fixes. With options for spreadsheet-only workflows or light automation (Zapier/Make/Python), the guide helps marketing, SEO, and comms teams transform ad-hoc testing across ChatGPT, Claude, Gemini, and Perplexity into a consistent, comparable dataset that drives decisions instead of hunches.

The guide walks you through execution end-to-end: Step 1 defines use-case clusters and builds 15–20 prompts that represent real customer journeys; Step 2 runs controlled tests across the major LLMs and records verbatim answers with sources; Step 3 scores each answer on frequency/sentiment/accuracy, captures examples, and converts raw scores into brand/competitor rollups; Step 4 operationalizes the program with a weekly cadence—automating runs, pushing alerts when scores drop, and publishing trendlines so stakeholders can see movement over time. Tips emphasize locking prompt wording, testing on the same model versions, screenshotting results for auditability, separating brand vs. product mentions, and identifying “lift levers” (owned content updates, Wikidata/knowledge panel fixes, publisher outreach). Completion criteria include: a stable prompt set, a multi-LLM baseline with competitor comparisons, a running trend dashboard, and a documented playbook for remediation when citations underperform.

Use this for:
‧ Proving whether (and how) LLMs cite your brand today, with defensible evidence you can show executives
‧ Turning competitive gaps into a prioritized backlog of content/entity fixes that improve future AI answers
‧ Establishing a repeatable, low-lift weekly monitoring loop with alerts, trends, and on-call ownership