Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:

🚨 AI Top Story: Most AI tools generate smart-sounding answers—but not always accurate ones. This week, we break down how RAG fixes that by letting your model pull from your own content.

📊 One Quick AI Hack: Broken dates, bad emails, duplicate rows? AI can clean your contact list faster than you can say “CSV.”

🎯 AI Tool Of The Week: Google Analytics just got smarter. GA4 now uses AI to turn raw data into real marketing decisions.

🌟 Creator Spotlight: Adam Biddlecombe A breakdown of the terms everyone’s using—but not everyone fully understands.

How Smart Marketing Teams Are Using RAG

This Is How Better AI Content Gets Made

If you’ve been experimenting with AI tools in your marketing workflow, chances are you've run into some limitations.

Sometimes outputs generated by LLMs might sound impressive, but the details aren’t always accurate. This can be a big problem when it comes to product details, or brand specifics.

That gap between what the model can do, and what you actually need it to say usually comes down to one thing: context.

If you read our piece last week on MCPs, this might sound familiar. Both RAG and MCP are tackling the same underlying challenge: how to give AI access to the right context so it can actually be useful. But the difference lies in where, and how they’re used.

MCP, or Model Context Protocol, is designed for AI agents. It gives them a standard way to connect with tools, APIs, and systems so they can take action. It’s the infrastructure for doing.

RAG, or retrieval-augmented generation—is more for LLMs. It helps them generate more accurate, informed responses by pulling in relevant internal knowledge. It’s the infrastructure for knowing.

RAG works by connecting the model to your content. Things like help docs, campaign briefs, landing pages, CRM data, or product descriptions. That’s so when it generates a response, it’s not guessing or defaulting to generic internet knowledge. It’s pulling from your actual source material. The result is output that’s more accurate, more specific, and more aligned with your brand.

This kind of setup is what turns AI from a novelty into something marketers can actually depend on. It’s how content teams generate on-brand copy faster. How support teams deliver better answers without relying on pre-scripted flows. How product updates, campaign messaging, and brand positioning stay consistent across AI-powered touch points—simply because the model is always referencing the same source of truth.

And because RAG runs on your own content, you control what the model sees, and when it gets updated. That means less time re-prompting or tweaking outputs, and more time putting AI to work where it actually helps.

For teams already creating a steady stream of content, it’s a way to get more mileage from what you’ve already built.

And the implementation isn’t as heavy as it sounds. Most setups involve storing your content in a vector database, indexing it so it’s searchable by semantic meaning, and retrieving the most relevant snippets whenever the model generates a response. This means you’re not having to retrain a model, you’re just able to give it the right context in real time.

At a basic level, it involves three parts: first, gathering the content you want the model to reference. Things like product docs, help articles, internal guides, or campaign copy. Then, storing that content in a vector database so it can be searched by meaning, not just keywords. And finally, using a retrieval layer to feed the most relevant pieces into the model whenever it generates a response.

That retrieval layer can be custom-built, but more and more tools are making it easier. For example, OpenAI’s GPTs with “custom instructions” and file uploads can function as a lightweight RAG setup. On the more advanced side, platforms like LangChain or LlamaIndex let you connect structured content to a model using APIs and logic flows—no retraining required.

If you’re building in-house workflows or using custom assistants, this structure is what makes the outputs more grounded, useful, and on-brand.

🔍 Google is testing AI search on its homepage - Google is experimenting with a new toggle that lets users switch to an AI-powered search mode.

🧠 AI LLMs Learn Like Us, But Without Abstract Thought - New research shows large language models mimic human learning patterns, just without the ability to form high-level concepts the way we do.

📈 AI Ads & Strategy Trends [May 2025] - A current look at how brands are refining AI ad strategies in response to changing platform trends.

🤳 AI in Social Media: How To Use AI To Connect With Your Audience - Shopify breaks down practical ways brands are using AI to improve engagement, streamline content, and show up more consistently on socials.

🎯 The Artificially Intelligent Enterprise - Replace Your Word Processor with ChatGPT

☕ AI Tangle - Replace Your Word Processor with ChatGPT

How to Clean Dirty Data with a Single Prompt

If you’ve ever inherited a messy CSV full of inconsistent emails, broken date formats, or 14 ways to spell "United States," you know the pain of manual data cleaning. AI can now handle most of that grunt work for you.

Whether you're prepping a lead list, organising campaign data, or importing contacts into a CRM, this trick helps you go from chaos to clean with a single prompt.

• Export your messy spreadsheet as a CSV
  Open your file in Google Sheets or Excel and download it as a .csv.

• Upload the CSV into an AI tool that supports file input
  Tools like GPT-4 with Code Interpreter (Advanced Data Analysis) or Claude 3 let you upload CSVs directly.

• Paste this prompt alongside your upload:

Please clean and standardise the attached contact list.

Here’s what I need you to do:

Remove duplicate rows (exact and near matches).

Standardise all country names to “United States”.

Remove rows missing an email address.

Fix obvious email formatting issues (e.g., missing .com).

Format all dates as YYYY-MM-DD.

Capitalise first and last names properly.
Return the cleaned version of the table in the same format.

• Review, copy, and reimport
  Once the model returns the cleaned version, you can copy it back into your sheet or download the corrected CSV.

Turns Data Into Direction With GA4

Google’s default analytics platform is no longer just about dashboards and pageviews, it’s evolving into a powerful AI-driven decision tool.

GA4 (Google Analytics 4) now includes machine learning features that help marketers spot trends, anticipate user behavior, and optimize campaigns without needing to dig through rows of data.

Why It’s Useful

For marketers dealing with fragmented data and shifting customer behaviour, GA4 helps bring clarity. Instead of just showing what happened last week, it surfaces what’s likely to happen next, so you can act faster.

Whether you’re trying to improve retention, optimise campaign performance, or build smarter segments for targeting, GA4’s AI features give you actionable insights without needing to run deep reports. It spots the patterns, predicts the outcomes, and helps you make more informed decisions, without adding multiple more tools to your stack.

Key Features:

Predictive Audiences: Segment users based on AI-driven metrics like conversion likelihood.

Anomaly Detection: Automatically flags spikes, dips, and unexpected patterns

Event-Based Tracking: Real-time measurement of user activity across web and app

Ad Platform Integration: Syncs with Google Ads to enhance bidding and audience targeting

Pricing:

GA4 is free to use for most businesses, with all core AI features included. There is also an enterprise version: GA4 360—which is available for organisations that need higher data limits and advanced capabilities.

👉 Click Here To Explore

ADAM BIDDLECOMBE  - A breakdown of the terms everyone’s using—but not everyone fully understands.

Your LLM probably hates You 😅