Prompt optimization should feel like engineering.
We spent years building LLM products and saw the same pattern over and over again. A prompt change looked promising on a small sample, then quietly increased cost, broke formatting, or failed on the next batch of real inputs. Teams ended up arguing from intuition because nobody had explored the space broadly enough or evaluated changes rigorously enough.
In production, that is not a writing problem. It is a search problem, an evaluation problem, and a decision problem. Search, because the space of viable prompts is huge. Evaluation, because careless testing creates false confidence. Decision, because the best prompt is rarely a single winner once quality, cost, and latency all matter at the same time.
The search space is even larger than most teams realize, because every model responds differently to the same prompt. A prompt tuned for GPT-4o exploits GPT-4o's particular strengths; run it on a cheaper model and the quality drop you see is not the model's ceiling. It is the penalty for a mismatched prompt. True intelligence arbitrage means optimizing the prompt for each target model independently and then comparing every model at its best. When you do that, the gap between “expensive” and “cheap” models shrinks dramatically, and the cost savings become real.
We believe systematic, statistically correct multi-objective prompt optimization should be standard engineering practice. That means broad search, disciplined evaluation, held-out validation, and a clear view of the non-dominated options instead of a hand-picked anecdote. It also means vendor control by default: prompts and evaluation data should go only to providers you choose.
We wanted a product that did exactly that for real teams building classifiers, extraction pipelines, summarizers, and agentic systems. And so EigenPrompt was born.
The goal is not to sound clever about prompts. The goal is to know what got better, what got cheaper, and whether the gain is real.
Strong opinions, backed by process
We do not think prompt optimization should be a black box or a vibe. These are the principles we are building around.
Search the space properly
Manual prompt tuning samples a tiny corner of a very large design space. EigenPrompt exists to explore that space systematically instead of relying on whatever rewrite happens to come to mind next.
Validate improvements honestly
Fast screening is useful, but confidence comes from statistically correct evaluation. We care about held-out validation because tuning and grading on the same examples is how false progress gets shipped.
Make the trade-off explicit
There is rarely one universally best prompt. There are non-dominated options across quality, cost, and latency. Showing the frontier is more honest and more useful than pretending there is a single magic answer.
We think the upside is huge. We also think honesty matters.
Why we are excited
Prompt quality has real leverage. Better instructions, examples, and output constraints can materially improve reliability, cut token usage, and make cheaper models viable without changing the rest of your application.
What keeps us grounded
Optimization is not magic. If the task is vague, the rubric is weak, or the dataset is noisy, the right move may be to fix the setup first. EigenPrompt should help expose those limits, not hide them behind good-looking charts.
Built by people who needed this themselves
EigenPrompt comes from shipping LLM systems in the real world, where reliability, cost, and delivery dates all matter at once.
Lead AI Engineer
A decade building ML systems that handle production-scale inference. Focused on turning optimization ideas into software that is fast, measurable, and reliable enough to use in real delivery environments.
AI Consultant
Years advising large organizations on how to move AI from proof of concept to production. Keeps EigenPrompt anchored to the commercial reality teams face when an impressive demo has to become a dependable product.
If prompt quality matters, intuition is not enough.
EigenPrompt helps teams move from ad hoc prompt edits to systematic, statistically grounded optimization so they can ship with more confidence and less guesswork.