Beyond the Code: How Marketers Can Harness the Power of Generative AI

In the intricate tapestry of artificial intelligence, generative models have emerged as the cutting-edge artisans, crafting text, images, and more with remarkable finesse. However, as any AI expert will confirm, crafting this art requires more than just raw computational power. For those of us developing next-gen AI products, you can think that precision tools like embeddings, prompt personas, prompt rules, and feedback loops are the paintbrushes that fine-tune our masterpieces. If you're a marketer or technologist consuming this technology, understanding these tools is pivotal to leveraging Generative AI's full potential in your offerings.

In this post Ill break down what these things mean and why they should matter to those looking to leverage this technology.

Embeddings - the DNA of data interpretation

Picture a vast, intricate library where each piece of information—be it text, image, or sound—is not a book but a numerical vector. These vectors, known as embeddings, capture the essence and relationships of data in a way that's deeply intuitive for AI models.?

For tech leaders, leveraging embeddings means accessing a treasure trove of semantic relationships. Imagine enabling your platform to understand that "ROI" and "Return on Investment" carry the same meaning or that a picture of a "golden retriever" and "labrador" might appeal to the same audience because they're both popular dog breeds. Embeddings make these associations possible, offering an edge in applications like content generation and recommendation systems.

Prompts: Personas & Rules to help tailor AI's Creative Genius

Think of personas as setting the mood for our AI. If you're crafting marketing content and want it to resonate like Apple's iconic minimalist style, you'd set the persona to "Apple Brand Voice." For marketers, a tailored persona means content that resonates deeply with target audiences, elevating brand voice and positioning. It's the difference between generic content and content that carries a unique brand signature.

Need some examples? Here are a few funny ones I came across recently:

Sir ChuckleBot, the Jester of Jargon: A medieval court jester with a penchant for modern tech lingo. Imagine Shakespearean eloquence mixed with Silicon Valley buzzwords: "Hark, fair marketers! 'Tis time to pivot thy funnel and leverage the cloud's mighty synergies!"

Captain CrunchData, the Pirate Analyst: A swashbuckling pirate who's as passionate about analyzing B2B metrics as he is about hunting for treasure. "Ahoy, mateys! Let's plunder the analytics seas and capture those elusive ROI doubloons!"

Lady Llama-Link, the Networking Noble: An aristocratic llama with an insatiable appetite for B2B networking events and LinkedIn connections. "Darlings, one doesn't simply graze on grass. One must also graze on golden opportunities! Let's connect, shall we?"

Prompt rules are the guidelines we set for our AI artist. Need a white paper without industry jargon? Set that as a rule. For consumers of this technology, having control over AI outputs ensures that the generated content aligns with brand guidelines and industry standards. It's also a safety net, ensuring AI doesn't stray off the desired path.

Feedback Loops: The Continuous Refinement Cycle

This is the iterative process where AI learns from user feedback, much like how a writer revises drafts based on editor feedback. Since most marketers won't be looking to tune the model itself, the feedback loops are helpful to help further refine the outputs, to best suit the use cases. In the B2B space user preferences can be niche and ever-evolving.

Feedback loops ensure that your tool stays agile, adapting to the unique needs of your B2B clientele and ensuring long-term product relevance. Coupled with evaluation frameworks, they also guarantee confidence around quality outputs - so the good continues to stay good and the bad gets better.

Some fun examples of feedback loops below:

The Meme-o-Meter: After every AI-generated content piece, users are prompted with a fun scale ranging from "Straight-faced Spock" to "Laughing Leo (DiCaprio, of course)" to rate the humor and relevance of the content. Based on user reactions, the AI then tweaks its future content, occasionally quipping, "Aiming for more 'Laughing Leo' next time!"

The Waffle vs. Pancake Scale: To gauge the depth and fluffiness of the content, users slide between a "Flat Pancake" (too shallow) and a "Deep Waffle" (just the right depth and fluff). The AI, learning from feedback, might jestingly respond, "Got it! More syrupy depth, less pancake flatness coming up!"

For those of us at the forefront of AI product development, the goal is clear: craft systems using generative models, that aren't just technically brilliant but also deeply attuned to user needs. Embeddings, personas, rules, and feedback loops are our compass in this journey.

For the marketers and tech leaders charting the future of B2B solutions, understanding and leveraging these tools can be the difference between an AI feature that's "nice to have" and one that's indispensable. As AI continues to redefine the digital landscape, these tools offer a promise: a future where AI doesn't just generate content, but crafts narratives that drive engagement, loyalty, and growth.

And remember, if our AI ever starts making coffee for us, let's just hope it doesn't ask, "Have you tried turning it off and on again?" before brewing! ??? Keep innovating and laughing along the way!