Increase Contrast, Not Volume

Hook

Similarity in, similarity out.

Problem

LLMs are highly sensitive to the context they are given, but they are also strongly pulled toward familiar patterns. When tasks share the same audience, framing, tone, and constraints, the outputs begin to converge.

This is why landing pages start sounding alike, blog posts repeat the same shape, and code suggestions lean on the same implementation patterns. The model is not necessarily failing. It is following the strongest signals available.

Users often respond by asking for the output to be more creative, more original, or more detailed. But more volume does not necessarily create more variation. If the underlying context remains similar, the model will continue producing answers from the same neighbourhood of likely outputs.

The real lever is contrast. If you want a different result, you need to provide meaningfully different conditioning information.

Why it matters

Without contextual contrast, LLM outputs tend to collapse into defaults.

In marketing, that produces generic copy and weak positioning.
In engineering, that produces generic implementations and hidden assumptions.
In both cases, it increases editing effort because the user must force distinctiveness back into the output.
It also reduces strategic value because the answer sounds competent while failing to reflect what is unique about the business, the system, or the audience.

Contrast matters because it changes the model's decision space.

Instead of choosing between many generic possibilities, it is guided toward a narrower, more specific, and more differentiated answer.

Signals you are here

Different outputs share the same structure or tone
Landing pages feel interchangeable
Blog posts repeat the same arguments
Code suggestions default to familiar patterns
The model uses brand-safe but undifferentiated language
Requests for more creativity produce only superficial variation

Anti-patterns

Asking for originality without changing the brief
Using the same audience and framing across multiple tasks
Relying on adjectives like creative or unique as the main instruction
Leaving the model to infer what makes this task different
Using generic prompts for differentiated work

Try this

Change at least two dimensions of context before regenerating
Specify the audience and their exact tension
State what language, framing, or patterns to avoid
Anchor the task in distinct evidence, constraints, or examples
Use structure as a lever, not just tone
Tell the model what makes this piece different from the last one

Example

A marketer asks:

> Write landing page copy for our IAM service.

That prompt invites default SaaS security language.

A higher-contrast version is:

> Write landing page copy for a CTO at a mid-sized SaaS company dealing with identity sprawl after acquisitions. Lead with operational control and audit readiness, not fear. Avoid generic cybersecurity buzzwords. Do not use the phrases digital landscape or words seamless, robust, or powerful.

A developer asks:

> Write a webhook handler.

That prompt invites a default implementation.

A higher-contrast version is:

> Write a webhook handler for a high-volume Node service running in Lambda. It must be idempotent, safe for retries, reject malformed payloads early, emit structured logs, and avoid class-based design.

In both cases, contrast produces output that is more specific and more useful.

Reflection prompt

What meaningful differences have I given the model, beyond just asking it to say more?

Increase Contrast, Not Volume

Hook

Problem

Why it matters

Signals you are here

Anti-patterns

Try this

Example

Reflection prompt

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