Roadmap
Honest AI evaluation grounded in reproducible methodology
What's live now
uatgpt publishes model comparisons, prompt engineering patterns, and cost analysis for production AI deployments. Every benchmark includes methodology documentation — the exact prompts, scoring rubrics, temperature settings, and measurement dates. The token counter shows how different models tokenize the same input and what that means for your bill.
What we're building
Priorities shift based on what the data shows and what readers need. This is where we are investing effort now and what is being evaluated for future development.
Live cost calculator
PlannedReal-time pricing comparison across providers — input your monthly token volume and task type, get the actual cost breakdown including retry overhead and output validation.
Prompt pattern library
In progressDocumented structural patterns (chain-of-thought, few-shot, system prompt architecture) with measured performance across model families — not tricks, but transferable methodology.
Latency benchmark dashboard
ResearchingP50/P95/P99 latency distributions for major providers, updated regularly, because averages hide the tail latency that breaks user-facing applications.
Model selection guide
PlannedDecision framework that maps task requirements to model recommendations — factoring cost, latency, output quality, and context window needs.
Long-term vision
The AI tooling space is moving fast enough that most published comparisons are stale within months. uatgpt is building toward a continuously verified reference — where every claim has a test date, every benchmark has a methodology, and outdated information is flagged rather than left to mislead.
The practitioners who build on these APIs need data, not opinions. They need to know which model handles structured output generation reliably at scale, what the true cost per useful token is after accounting for retries, and how latency distributions change under load. We publish that data because the model providers do not, and the cost of choosing wrong compounds fast.
Long-term, we aim to be the evaluation methodology standard — the source that other publications cite when they need rigorous model comparison data.
Methodology transparency
Every benchmark published with complete methodology — prompt sets, scoring rubrics, sample sizes, and measurement dates. Reproducible by anyone with API access.
Cost truth
Actual deployment cost analysis including hidden factors: retry rates, token overhead, output validation compute, and the gap between list pricing and real-world bills.
Practical patterns
Prompt engineering and integration patterns that transfer across model families because they are grounded in how language models process information, not in version-specific behaviors.
How this fits
uatgpt serves as the AI evaluation vertical — the domain that answers "which model should I use and what will it actually cost?" with measured data. Cross-domain connections include automation patterns (botneve.com) where AI models are deployed in production workflows, and the technical documentation methodology shared across all seven domains.
How we decide what to build
Utility over volume
We add a tool or article when it completes a user task that is currently unserved or poorly served. We do not publish to fill a content calendar.
Depth over breadth
One article with tested data tables and original analysis is worth more than ten articles that restate commonly available information. We publish less, but each piece earns its place.
Evidence over speculation
Roadmap items move from research to planned to active based on what the data shows — reader behavior, content gaps identified in search, and the competitive landscape. Intuition starts the investigation; evidence finishes it.
Tools compound
An interactive tool that solves a recurring problem creates a return visit. A static article that answers a one-time question does not. We prioritize building tools that bring readers back.