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At a Glance
Big Idea: AI outputs aren’t bad because the models are weak. They’re bad because we ship first drafts.
Why It Matters: As AI moves from “assistant” to “builder,” silent errors scale faster than teams can catch them. Without feedback loops, confident but wrong outputs will reach production, across code, specs, and strategy.
The Big Idea: AI Quality Is a System, Not a Prompt
A few weeks ago, I used AI to generate a Product Requirements Doc (PRD) for a new internal feature.
The doc looked excellent:
Clear goals
Logical user flows
Well-written requirements
Confident timelines
If you skimmed it, you’d approve it.
But when engineers and stakeholders reviewed it, the issues surfaced:
Critical edge cases were missing
System dependencies were assumed, not validated
Security and compliance weren’t mentioned
Non-functional requirements were completely absent
Nothing was obviously wrong.
Which is exactly what made it dangerous.
That’s when it clicked for me:
AI quality isn’t about better prompts.
It’s about better systems.
The Real Failure Mode: One-Shot AI
“One-shot AI” works when the cost of being wrong is low.
Emails. Brainstorming. Rough drafts.
But the moment you use AI for:
production code
technical documentation
API contracts
product strategy
…the first draft becomes the most dangerous one.
In real systems, errors don’t fail loudly.
They fail quietly.
And AI is very good at sounding confident while being wrong.
That’s why better prompts alone won’t save you.
There’s a deeper risk here that most teams miss.
AI systems don’t usually fail with obvious errors.
They degrade quietly through small assumptions, missing constraints, and unchecked outputs that compound over time.
In production AI systems, this is known as drift.
In everyday AI usage, it looks like this:
Specs that slowly diverge from reality
Docs that feel right but mislead
Strategies built on assumptions no one validated
The danger isn’t bad output.
It’s unchecked output becoming the foundation for the next decision.
The Shift: From Prompting to Feedback Loops
The best teams aren’t prompting harder.
They’re running a simple loop:
Generate → Critique → Refine
This loop turns AI from a content generator into a thinking partner.
How the Quality Loop Works
1. Generate (with constraints)
Don’t ask for a generic doc.
Ask for structure and boundaries.
Draft a PRD for {feature} including:
- goals
- user stories
- success metrics
- rollout plan
- known constraints
Constraints reduce hallucinations before they start.
2. Self-Critique (the missing step)
Now ask the AI to evaluate its own work:
Review this PRD and identify:
- missing edge cases
- unrealistic assumptions
- unclear ownership or dependencies
- risks that could delay delivery or impact users
This forces the model out of creation mode and into evaluation mode.
That mode switch is where quality jumps.
This works because generation and evaluation are fundamentally different tasks.
When you ask an AI to “create,” it optimizes for fluency and completion.
When you ask it to “review,” it optimizes for gaps, risks, and failure modes.
Most quality issues disappear not because the model got smarter,
but because you forced it into the right mode.
3. Refine (based on feedback)
Finally, feed the critique back in:
“Revise the PRD to address these issues.
Optimize for engineering feasibility and operational reality.”
This is where “looks good” turns into “actually usable.”
Real-World Impact
After rebuilding our PRDs with this loop:
Hidden assumptions surfaced early
Engineering risks became explicit decisions
Review cycles dropped from weeks to days
Fewer last-minute scope changes during delivery
Same AI.
Same people.
Different system.
Prompt Templates: The AI Quality Loop
Product & Strategy Docs
Generate
Draft {document type} for {feature} with {explicit sections}
Critique
Identify gaps, risks, missing assumptions, and dependencies
Refine
Revise to address issues with realistic constraints
Technical Writing
Generate
Draft documentation for {feature} targeting {audience}
Critique
Identify unclear steps, missing prerequisites, or assumptions
Refine
Improve clarity and completeness
Planning & Decision-Making
Generate
Create a plan for {initiative}
Critique
Identify unrealistic timelines, resource gaps, and risks
Refine
Adjust based on constraints and tradeoffs
Same loop.
Every domain.
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AI has changed how consumers shop by speeding up research. But one thing hasn’t changed: shoppers still trust people more than AI.
Levanta’s new Affiliate 3.0 Consumer Report reveals a major shift in how shoppers blend AI tools with human influence. Consumers use AI to explore options, but when it comes time to buy, they still turn to creators, communities, and real experiences to validate their decisions.
The data shows:
Only 10% of shoppers buy through AI-recommended links
87% discover products through creators, blogs, or communities they trust
Human sources like reviews and creators rank higher in trust than AI recommendations
The most effective brands are combining AI discovery with authentic human influence to drive measurable conversions.
Affiliate marketing isn’t being replaced by AI, it’s being amplified by it.
The Hidden Benefit Most People Miss
As AI-generated content becomes the default input for more systems,
docs feeding plans, plans feeding builds, builds feeding decisions
unchecked outputs start reinforcing each other.
This is how quality collapses:
not in one dramatic failure,
but through a thousand “looks good to me” approvals.
Feedback loops aren’t about perfection.
They’re about preventing silent degradation at scale.
Before you go
AI is very good at producing answers.
It’s terrible at knowing when those answers are safe to ship.
That gap is your responsibility.
The difference between low-leverage AI and high-leverage AI isn’t intelligence.
It’s process.
The best teams don’t trust AI less,
they just verify it more.
So if there’s one rule to take away from this:
Never ship a first draft, especially when AI wrote it.
That principle will outlast every model release.