The “3 AI Terms a Week Keep the Buzzwords at Bay” edition, Part 4

Welcome back to our summer series - where I break down three AI terms a week to help leaders like us make AI and summer a bit more digestible.

This week we’re focusing on how good AI outputs really are - and what shapes that quality behind the scenes.

1️⃣ Output Quality

🧠 What it is: Output quality is how accurate, useful, and relevant the AI’s response is for a given task. It’s the difference between a helpful assistance or confusing nuisance (yes AI is sometimes a nuisance for me too 🤣)

🚫 What it’s not: It’s not consistent. AI quality varies by model, use case, data, and how well the input is framed.

🛠️ Real example: Two teams ask the same question in two different ways - one gets a concise action plan, the other gets a vague summary. Same model, different outcomes.

📈 Why this matters to us: Quality is what earns trust. If we rely on AI for research, writing, or important decisions - we need to know what shapes quality and how to evaluate it.

2️⃣ Feedback Loop

🧠 What it is: A feedback loop happens when AI-generated outputs are reused as future inputs - often without fresh data or review - leading to self-reinforcing patterns or errors.

🚫 What it’s not: It’s not actual learning. Most AI doesn’t improve on its own - it can just get caught in a loop of its own making.

🛠️ Real example: A marketing team uses AI to generate headlines, then trains future prompts on past AI outputs. Over time, the language becomes repetitive and less impactful.

📈 Why this matters to us: Feedback loops can quietly erode output quality over time. To break the loop, we need to keep humans in the loop - reviewing, editing, and refreshing inputs regularly.

3️⃣ Guardrails

🧠 What it is: Guardrails are the policies, filters, and design choices that keep AI behavior within acceptable bounds - protecting quality, safety, and brand integrity.

🚫 What it’s not: It’s not just blocking bad outputs. Good guardrails also protect from subtle issues like repetitive phrasing or vague answers.

🛠️ Real example: An internal knowledge assistant is configured to cite sources, flag uncertainty, and avoid answering legal questions - ensuring responses stay accurate and appropriate.

📈 Why this matters to us: Guardrails are essential to sustain output quality and prevent feedback loops from drifting off-course. Ask: what controls are in place - and who’s tuning them?

🧠 Bringing it All Together - Why This Matters for Leaders Like Us

This trio - output quality, feedback loops, and guardrails - gives us kind of a playbook for responsible, high-performing AI.

A theme I will explore further, because quality doesn’t just happen - it’s designed, monitored, and maintained. As leaders, we need to remind ourselves to ask not just what AI says, but how and why it gets there.

More terms next week. Got a suggestion? Hit me with your feedback loop (no pun intended).

Hey, I’m Marco - I write, speak and train execs on #marketing and #AI. Enjoyed this? 👉 Connect or follow me - Marco Andre