Send us a textThink AI just makes things up? You’re right, it often does. But there’s a fix. It’s called RAG, and it helps AI ground its answers in real, relevant information. In this episode, I’ll explain how it works and why it’s essential if you rely on AI for content or communication.In today’s episode, I'm talking about something you’ve probably used without even knowing it: RAG – Retrieval-Augmented Generation.Most large language models, like GPT, Claude, Gemini, work by predicting the next word in a sentence. They’ve been trained on huge datasets, everything from books to websites, forums, Reddit commentary, podcasts, and Wikipedia, essentially everything on the Internet. But once that training is done, the model doesn’t learn anything new unless it’s retrained. That’s called being a static model.Here’s where RAG comes in. Retrieval-Augmented Generation is a technique that lets these models access external information, like documents, PDFs, databases, or web content, at the time of your query. Instead of relying only on what the model remembers from training, RAG lets it retrieve relevant information first, and then generate a better, more accurate answer.Need More Support? If you’re ready to explore how AI can make your marketing smarter and more efficient, check out my Professional Diploma in AI for Marketers. Or, if you’re looking for in-company training, I can help get your team up to speed. Use the code AISIX10 for a special discount just for podcast listeners. https://publicsectormarketingpros.com

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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