> For the complete documentation index, see [llms.txt](https://faq.liquidandgrit.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://faq.liquidandgrit.com/extras/ai-guidelines.md). # AI Guidelines ## Key Principles * AI is a tool for acceleration, not a replacement for original analysis and writing. * Start with low-risk uses and expand only with proven reliability. * All AI outputs must be validated by humans before going to clients. * External-facing work must never read as AI-generated. * Let all members of a project know when you’re using AI so data can be double-checked. * Internal communications generated by AI should still be edited to be concise and accurate. ## Tools * We have business accounts with Claude and ChatGPT. Contact for access if you’d like to use them. * These business accounts ensure that our inputs are not used for training data. * Feel free to test other tools on your own, but confirm with management before bringing them into production. ## Prompting * Be specific and detailed in your prompts. * Save recurring prompts in .txt or other note documents for later use. * Ask AI how to improve your prompts. * Be ambitious! ## Best Use Cases ### Preliminary Research * Get quick background on new topics or professional roles * e.g., Understanding what metrics are most important for ad purchases * Great for understanding what’s important to your target audience * Learn about key players in a market or genre * Get baseline assumptions for key benchmarks * e.g., Expected conversion rates, churn rates, etc. ### Planning * Make a plan for tackling a problem or analysis * Outline steps for how to get data and how to process it * Describe criteria for evaluating data * Create documentation for project plan ### Data Collection * Pulling data from Sensor Tower via API key, L\&G via API, and public sources * NOTE: Sensor Tower’s API delivers revenue in cents. AI often mistakes this for dollars. Make sure you validate revenue data pulled via ST API. * Scraping data from the internet and social media * Sometimes AI is better used to write Python scripts to scrape data rather than to do the scraping itself * Gut-check, back-of-the-napkin math ### Data Management * Writing AppScripts and cell formulas for Google Sheets. * Cleaning data or identifying inconsistencies. * Bucketing data into categories. ### Data Analysis and Pattern Recognition * Identifying trends from datasets * Summarizing initial takeaways from raw data ### Report Structure and Organization * Guiding structural outlines for reports * Suggesting logical flow and section ordering * Identifying gaps in coverage or analysis * Reorganizing existing drafts for clarity * Identifying preliminary points for summaries ### Editing * Proofreading for spelling and grammar issues * Checking for logical inconsistencies * Identifying missed opportunities to tighten narratives ## Restricted Use Cases ### Private Contracts * Verify whether contracts have any restrictions on AI usage at the start of any private contracts ### Writing and Insights * AI should not write final text for deliverables * All writing that reaches clients (including messages and emails) must be edited by a human at a minimum ### Signs To Stop Using AI * When outputs require more editing than writing from scratch * When validation is taking longer than manual data collection ## Validation ### Data * Document the source and method for all AI-assisted data collection * Check samples of any AI-pulled data against primary sources * Flag and investigate any data point that seems inconsistent * Responsibility for accuracy of AI-pulled data is shared between the person who sourced the data and the project’s lead ### Deliverables * Ensure no text reads as AI-generated (generic phrasing, repetitive structure, excessive em dashes) * Look out for confident claims with no clear sourcing * Verify every data point