This week’s focus: reflection and differentiation.
As AI makes it easier for competitors to copy what we build, the real advantage comes from how we learn and evolve. In Module 2 of my Agentic AI series (Module 1 linked here), I unpack the Reflection Design Pattern—how systems (and marketers) improve through structured feedback loops. It’s the same dynamic that separates strong brands from forgettable ones: the ability to listen, adapt, and get better over time.
Let’s dive in 👇
🚨Signal or Noise? Dissecting recent AI headlines
🧠 Brand Moats Are Weakening — How to Strengthen Yours w/AI
🧩 Project Playground: Reflection Design Patterns in Agentic AI
📣 Word of the Week
🚨Signal or Noise? Dissecting recent AI headlines
Summary: WPP has committed $400 million over five years to integrate Google’s AI assets (like Gemini and video generation) into its agency services.
Why it matters: This signals that AI is becoming a core capability in agency offerings—and B2B marketing teams will likely expect similar built‑in AI horsepower from their service partners.
Summary: Warmly released a tool that continuously builds, updates, and ranks high-value accounts and contacts using AI and real-time signals.
Why it matters: Marketers juggling account lists and prioritization would benefit from AI doing the heavy lifting—freeing you to spend more time on strategy and messaging.
Summary: More than half of tech buyers now rely on chatbots or AI tools as a primary source for discovering vendors, surpassing traditional search engines.
Why it matters: It means your SEO and content must evolve—not just for keywords, but for AI prompts and conversational discovery to be visible in that new buyer journey.
🧠 Brand Moats Are Weakening — How to Strengthen Yours w/AI
I came across a brilliant piece by Liza Adams this week that reframed how I think about brand moats in the age of AI. Her core idea: your competitors can copy your features, but they can’t copy years of consistent care.
One restaurant became a legend not because of clever marketing—but because it systematized moments of surprise and delight. The owner created a team whose only job was to listen for emotional cues from guests and act on them—like running out to buy New York street dogs for visitors who mentioned they hadn’t tried one yet. Those weren’t random acts of kindness; they were part of the system.
Graphic re-created from Liz’s article
Now imagine AI doing that at scale—surfacing patterns in customer feedback, interactions, or even support tickets to flag the perfect moments to go above and beyond.
Most companies use AI to strip away human touch. The defensible ones use it to amplify it.
How to apply this as a product marketer:
Use AI to listen better—analyze feedback, survey data, or call notes for unmet emotional signals.
Act on them with intent. Send personalized follow-ups, connect them with peers, or share resources that solve their exact challenge.
Build a rhythm of care. Anyone can copy a one-off gesture; no one can fake long-term trust.
AI should free us to be more human, not less.
🧩 Project Playground: Reflection Design Patterns in Agentic AI
Quick Recap for my new subscribers: Each week for the next several weeks, this project playground feature will follow the latest Agentic AI course syllabus from DeepLearning.AI, taught by Andrew Ng (an AI legend), recapping what I learn each week through a product marketing lens. Part two of this project will be actually building something with me. Jump to last week here.
💡 What Reflection Really Means
Reflection acts as a self-feedback loop for AI. The result? Smarter reasoning, stronger performance, and outputs that improve with every loop.
It can involve one or multiple LLMs—one generates, another critiques, a third revises.
External feedback (from users, performance data, or campaign results) often drives the biggest improvement.
Reflection workflows consistently outperform direct generation when built intentionally.
For product marketers:
Think of reflection like an automated post-mortem. After generating a campaign brief, an agent can review it against launch goals, customer personas, and tone consistency—then refine before you ever hit publish.
⚙️ Prompting Patterns Refresher
Zero-shot: No examples of desired output —> “Write me an email about trees”
One-shot: One example. —> “Write me an email about trees, here’s an example of what I’m looking for:...”
Two-shot: Two examples…
Few-shot: Multiple examples to guide the model
Reflection can layer onto any of these—acting like a second draft pass that improves precision and quality.
For product marketers:
If you’re using AI to write messaging variations, add a reflection step that checks for clarity, differentiation, and tone alignment before you review.
🧮 Evaluating Reflection Outputs
Using an LLM as a judge can work, but comes with caveats:
Position bias—models often prefer the first answer.
LLMs handle objective checks better than subjective ones.
Grading with a simple rubric (“Does it include X? Yes/No”) yields more consistent evaluations.
For product marketers:
When evaluating AI-generated copy or positioning statements, use a rubric to measure specific criteria—like value prop clarity or customer pain alignment—rather than relying on “gut feel.” AI doesn’t have a gut; it has data.
🧩 Success Tips
Clearly label the reflection action (e.g., “Now review your previous answer and identify one flaw.”)
Specify evaluation criteria so the model knows what to check for.
Use reasoning models at reflection checkpoints for best results.
Utilize feedback from external sources. For example, imagine using customer engagement data or campaign performance metrics as external feedback. Your agent could analyze why one email variant outperformed another, then use that insight to rewrite future copy or refine messaging automatically.
📣 Word of the Week
Reflection turns AI from automation into iteration.
It’s the difference between a one-and-done output and a self-improving system—
a pattern every product marketer should master when building agentic workflows that get sharper over time.
As always, stay curious and have fun!
Best,
Skyler Neal
