Smartest AI models for Content & Brand

“The model perfectly followed all instructions and constraints, including the strict word count and negative constraints. The tone is highly authentic, avoiding launchy clichés, and the output is essentially production-ready.”

We just shipped our first evaluation dashboard. The core functionality allows users to compare different prompts across multiple LLMs side by side. It is a foundational step toward making model performance more transparent and measurable.

I want to be clear that the product is still very early and quite rough around the edges. We are building in the open, which means you will encounter bugs and missing features as we iterate. Our immediate focus for the next update is adding role-based benchmark packs to help teams test for specific use cases rather than generic performance.

If you are working on prompt engineering or model selection, I would appreciate your feedback on the current interface. We are trying to solve the problem of subjective evaluation, and your perspective helps us decide where to focus our development time next. Thanks for following along.

“The response perfectly follows all constraints, provides a highly specific and vivid example, and nails the requested tone. The only minor flaw is the 'uncompiled code' analogy, which is technically nonsensical (uncompiled code cannot run) and might slightly alienate the CTO audience.”

Shipping an LLM feature because the prompt "looked good" after five manual tests is like merging uncompiled code and praying it runs. 

If you are building AI products, you cannot afford to eyeball your prompts and push them to production. 

Here is what happens: Your team writes a prompt for a customer support bot. They test it with standard inputs like "Where is my package?" and the output is flawless. They ship it. 

Two days later, a user pastes a 500-word furious rant in all-caps mixed with emojis and a threat to sue. Your system ignores the core instructions, panics, and replies with a cheerful, "Thanks for your feedback! Here is a 5% discount." Now you have a massive escalation on your hands.

Prompts are essentially non-deterministic code. You wouldn't skip unit testing for a new payment gateway, so why skip it here? 

Before a prompt hits production, it needs to run against a structured evaluation dataset. Test for edge cases, adversarial inputs, and formatting failures. Measure the failure rate objectively using an automated judge or exact match metrics.

Stop treating AI development like a magic trick. Treat it like software. Build an evaluation pipeline, measure the outputs, and only ship when the data proves it actually works.

“The response invents highly specific facts and fabricated anecdotes not present in the brief, requiring heavy editing.”

Subject: We've been building the wrong features

Hey founders,

For six months, we treated our support queue like a necessary evil—something to clear before getting back to "real work." Our roadmap came from whiteboard sessions where we'd pitch each other on clever features we thought users needed.

Then our head of support quit.

While covering her tickets, I noticed the same request appearing 47 times in three weeks: users wanted to bulk-edit metadata. We'd dismissed this internally because it "wasn't elegant" and didn't fit our vision of simplified workflows.

Built it in a weekend. Usage jumped 34% in two weeks.

Here's what changed: we now tag every support ticket and review the top 10 recurring requests monthly. If something appears more than 15 times, it goes straight to roadmap review—no debate about whether it's "interesting" enough.

The specific lesson: Your support queue is users literally telling you what to build, with their own time and effort. They're not asking for build systems to be 10% faster or animations to be smoother—they're showing you where your product actually breaks their workflow.

Our best features now come from ticket #2,847, not conference room C.