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 [email protected] 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