Unlocking Better AI Outputs: Why Tuning Matters (and Avoiding Hallucinations) The “GenAI for Marketers” Series

Welcome back to the “GenAI for Marketers” series.

If you’ve been following along: • Part 1 explained why Generative AI is becoming a non-negotiable part of modern marketing. • Part 2 unpacked how Large Language Models (LLMs) work (no technical jargon, promise). • Part 3 introduced prompting — the art of giving AI the right instructions.

Today in Part 4, we’re talking about something often overlooked: Tuning — the secret ingredient to going from "meh" AI output to "wow, that’s usable!" ...and how proper tuning can help you avoid one of the most common AI pitfalls: hallucinations.

What’s a Hallucination? Simply put, a "hallucination" happens when AI confidently generates an answer that sounds correct, but isn’t.

For example, you ask for a list of top-performing skincare campaigns from 2023 — the AI returns a beautifully written list. Only problem? Some of those campaigns never existed.

Hallucinations are especially risky in marketing when you're generating: • Product descriptions • Customer insights • Data summaries • Strategic recommendations

But here’s the good news: Tuning is one of the easiest ways to reduce hallucinations. Lowering the Temperature and adjusting Top-P settings pushes the model to be more grounded, cautious, and fact-oriented, which is critical when you need reliable outputs.

What Is Tuning? When you ask AI for an answer, you don’t just control what you ask (that’s the prompt). You can also control how the AI thinks before it responds.

Tuning is all about adjusting the model's "personality" for your specific task.

Imagine you’re briefing a creative agency: You can ask them to go wild and bring back 50 ideas (brainstorming), or ask for one sharp, polished headline. Tuning lets you do exactly that, but with an AI model.

The 3 Key Settings in AI Tuning. When using tools like ChatGPT, Google Gemini, or any LLM, there are usually three main dials behind the scenes. 

Let’s Make It Real: Marketing Examples

Scenario: Writing Social Copy for a Skincare Brand

Task: Write an Instagram caption for a new Vitamin C serum launch. 

Scenario: Email Subject Line A/B Testing

Task: Suggest 5 subject lines for a re-engagement email. 

Scenario: Customer Insight Summary

Task: Summarize customer feedback for your data team.

Why Tuning Matters for Marketers & Data Teams. In your day-to-day marketing work, you’ll deal with different needs:

• When you want precision: Use lower Temperature, lower Top-P, lower Top-K.
• When you want creativity, increase those values and give the model room to “think” freely.

The beauty is, once you understand these settings, you can design AI outputs like a pro — whether you’re drafting email copy, segmenting customer data, or even creating early creative concepts.

And when you're concerned about accuracy and reducing AI-generated hallucinations?

Lowering the Temperature and Top-P is your best friend.

 Can You Set These in ChatGPT?

In ChatGPT's free version, The app uses default tuning, which is designed for safe, balanced answers.

 In GPT-4 & Custom GPTs: You can fine-tune settings like Temperature when you: • Build custom GPTs.

• Use the OpenAI API (common for automated workflows).
• Use some pro-level tools like Jasper, Writer, or Adobe Firefly for content generation.

 Most day-to-day prompting focuses on the what (prompt).

If you want truly next-level results, tuning the how (settings) is where the magic happens.

Prompt = Brief.

Tuning = Creative Direction.

Together, they control whether your AI output is off the mark, agency-ready, or worse, hallucinated.

 #GenAI #PromptEngineering #MarketingOps #DataAnalytics #AIinMarketing #LLM #ContentAutomation #DigitalStrategy