Prompt Engineering Is Product Strategy Now

Prompting isn’t a side gig for your engineers.

It’s a core product muscle - and if you're a product manager in the age of AI, it's one you need to build fast.

Welcome to the Age of Instructions

In the AI-first world, prompts are no longer just inputs.

They're:

• Interfaces

• Instructions

• System design

• Brand voice

• Product thinking

• 
  

That one sentence you send to the model? It decides whether your user sees magic… or mess.

If you're still treating prompt engineering like a technical detail - you're already behind.

Why Prompts Are the Product

A prompt isn't just a line of text.

It's:

• The UX

• The logic tree

• The guardrail

• The outcome

Let’s say your app helps people summarize meeting notes.

Prompt A might say: "Summarize this transcript."

Prompt B, designed with product intent, says: “Give a crisp, bullet-point summary of key decisions and next steps from this transcript. Maintain professional tone. Use action verbs.”

Guess which one gets used, loved, and recommended?

The difference is prompt engineering - and that’s product strategy in disguise.

What Prompt Engineering Really Means (For PMs)

It’s not about writing poetic instructions for GPT.

It’s about:

• Knowing what *your user* is trying to do

• Translating that into clear logic + intention

• Building for accuracy, safety, tone, and flexibility

• Testing it like you’d test a feature

• Optimizing it like you’d optimize a funnel

And yes — versioning it like code.

Prompt = MVP (Minimum Viable Product)

Before you build that AI feature, try this:

• Write 3 prompt variants.

• Test them with 5 users.

• Observe what they expect, where they hesitate, what fails.

• Refine.

You've just run a product experiment - no dev needed.

Prompt-first prototyping is the fastest way to validate ideas in the GenAI world.

How We Do It in MyFortivo & Sattvahar

In both of my AI products:

• Sattvahar: uses prompt variants to personalize food recommendations based on health goals, allergies, and even mood.

• MyFortivo: relies on deeply tested prompts for self-reflection, PHQ-9 assessments, and journaling that feels human, not robotic.

We A/B test prompts like we would onboarding flows.

Because in AI — the wrong prompt doesn’t just mean a bad experience.

It could mean confusion, hallucination, or worse: user mistrust.

How to Make Prompt Engineering a Product Ritual

Here’s the exact workflow I use (and you can steal):

1. Start with the outcome

What does a “great” response look like to the user?

2. Design the tone and structure

Should it feel casual? Professional? Directive?

3. Write multiple prompt options

Explore different instructions. Add constraints. Test edge cases.

4. Run real-user tests

See what works in actual product flows.

5. Track success metrics

Prompt success rate, user edits, retries, satisfaction.

6. Log + version everything

Use tools like Vellum, PromptLayer, or Lovable to track evolution.

PM Red Flags to Avoid:

❌ “Let’s just use the OpenAI default”

→ Translation: we don’t care how this sounds or works.

❌ “We’ll fine-tune the model later”

→ That’s like saying, “We’ll fix UX in v3.” Nope.

❌ “The AI team will handle prompts”

→ You are the AI team now. Own the outcome.

In 2025, your users won’t ask: “What model is this built on?”

They’ll ask:“Why does it feel like this tool gets me?”

That feeling is prompt engineering. That experience is product strategy.

For PMs:

• Prompts = Product.

• Prompt testing = UX testing.

• Prompt ownership = PM leadership.

The best GenAI products aren’t the ones with the best models.

They’re the ones with the most thoughtful prompts.