Guide to Key AI Terms for Marketing and PR

AI has entered marketing and communications more powerfully than SEO did more than a decade ago. Suddenly, every day we hear new terms like RAG, LLMO, or embeddings, and before we master them, the next ones arrive. In this whirlwind of innovation, it’s easy to feel like you’re chasing a train that never stops at your station. 

Understanding this language is not just a matter of prestige or wanting to sound smart. It’s a skill that helps you learn, improve your knowledge, recognize opportunities, avoid empty marketing buzzwords, and build strategies that deliver results. That’s why we present you with a guide to the most important AI terms, explained clearly and practically, with examples from the world of marketing and PR, showing how to turn theory into practice. After reading this guide, you’ll be able to discuss AI with colleagues and clients confidently, understand key concepts, and recognize real opportunities for application. 

Large Language Model (LLM) - AI systems trained on massive amounts of text, capable of understanding and generating human-like language. They power chatbots, content generators, and many writing tools. 

Example: A team uses ChatGPT to prepare the structure and concept of blogs and social media posts, speeding up production while keeping the brand’s tone consistent. 

Transformer Architecture - The technology behind most modern AI language models. It allows them to recognize relationships between words in a sentence and generate more fluent, meaningful text. 

Example: An analytics team uses an AI tool based on this technology to organize report material more quickly, leaving more time for detailed data interpretation. 

Parameters vs. Tokens - Parameters represent the model’s “built-in knowledge” acquired during training, while tokens are smaller units of text (words or word fragments) that the model processes when reading input or generating output. In many AI tools, pricing depends on the number of tokens processed. 

Example: A manager optimizes prompts to use fewer tokens, reducing costs while maintaining content quality. 

Fine-tuning - The process of further training an existing AI model on your own data so it adapts to your brand’s specific tone, style, and terminology. This way, the model learns to create content that sounds like it comes directly from your organization. 

Example: A cybersecurity company fine-tunes a model on its research reports so that the generated texts carry the same expert tone and terminology as those written by its employees. 

Multimodal - A type of AI technology that can process multiple types of data simultaneously, such as text, images, audio, and video. This enables the creation of content that combines several formats and makes it easier to adapt materials for different channels. 

Example: A retail brand uses a multimodal model to turn product specifications into promotional videos with sound, subtitles, and visual effects, ready for social media publishing. 

Hallucinations - Situations where AI generates content that sounds convincing but is incorrect or made up. This can include inaccurate data, nonexistent sources, fabricated quotes, or entirely new “facts” that seem real. That’s why human fact-checking is essential before publishing.

Example: A PR agency team uses AI to draft press releases but introduces mandatory fact-checking to avoid including inaccurate data or invented statements. 

You can read the full article here. 

Source: Kliping.rs