From Chaos to Catalog: Prompt Evaluation
How to move from “try and pray” to reproducible decisions on which prompt to use, by model and by domain.
You spend almost 10 minutes crafting the perfect prompt so that an AI agent can process your data or write an email to your CTO. You run it and… meh. Not what you expected; actually, it’s wrong. Then your teammate pings you on Slack:
“Hey, try this prompt I made, it worked great for me.”
And indeed, that prompt gets the AI agent to do the job flawlessly. Maybe it was luck, the miracle of Saint GPT, or who knows — but it worked, and you save it for later.
Has that ever happened to you?
Of course it has. You’re far from the only one.
People share and reuse prompts on top of our databases without really knowing whether those prompts are right for our data. The results? A mixed bag:
Inconsistent responses across teams.
Incorrect or incomplete information creeping into operational decisions.
Endless time spent fine-tuning prompts through trial and error.
Token waste and reputation risk.
In short: without reproducible evaluation, every team will reinvent the wheel and pay for it.
This problem recently climbed up my personal list of engineering concerns, so I started digging for solutions.
As usual, once the problem becomes clear, the shape of a solution starts to appear.
What if there were a way for an organization to actually know which prompt is best for a given task?
Prompt Evaluation
Here’s the first idea that came to mind: set up an automated prompt evaluation system that can:
Run different prompt variants against multiple models.
Measure objective metrics: accuracy, completeness, hallucination rate, token cost, and correct formatting.
Classify results by model and data domain (customers, billing, logs, etc.).
Deliver a clear ranking: which prompt works best with which model and for what kind of data.
With that, a developer could choose the optimal combo instead of guessing. On top of that, evaluation should plug into CI to catch regressions when prompts or models change.
☝🏼 But hold on: do we really need to put together a dev team that spends who-knows-how-much time building a custom system like this?
My take: No, you probably shouldn’t. Avoid building your own prompt evaluation platform unless absolutely necessary. I only see two practical cases where it makes sense:
Small or experimental teams: You can evaluate manually, and that’s fine. A simple spreadsheet (yes, I know, Excel again!…) listing the prompt, what it was good for, and a place for people to rate or comment.
Companies needing traceability, governance, or audit evidence: In that case, investing in an evaluation framework may make sense… unless there’s already a managed PaaS that gives you that out of the box.
👉🏼 So my recommendation is to start with an existing system, ideally a managed service built by a company whose business is prompt evaluation.
Start with a POC using off-the-shelf tools. If, later, your integration, security, or scaling needs demand it, then evolve toward an ad hoc solution that plugs into CI/CD, your internal catalog, and PR workflows.
Before reinventing the wheel, it’s worth checking what’s already out there.
Existing Tools in The Market
Some options already offer prompt evaluation, management, and observability:
Amazon Bedrock Prompt Management and Prompt Flows: AWS (of course!) built an internal, large-scale automatic prompt evaluation system based on the “LLM-as-a-judge” paradigm. This flow lets you define evaluation criteria (clarity, completeness, formatting, etc.) and run them systematically against each prompt variant before going live.
Promptfoo: a framework for testing and evaluating prompts, with a viewer and CI integration.
LangSmith: focused on traceability, testing, and prompt management within the LangChain ecosystem.
PromptLayer: prompt registry and management with versioning and collaboration features.
OpenAI Evals: a framework for automated prompt and assistant evaluation using programmable tests. I know of companies that have extended it into their own in-house solutions.
From here, your job is to explore these options and pick the one that best fits your integration, privacy, and cost requirements.
Personally, based on those same criteria, I’ve got my eye on Amazon Bedrock Prompt Management. I’ll let you know how it goes.
Once you’ve chosen a tool, the key is to start small and measurable.
Next Steps
Here’s what I came up with, feel free to borrow it:
Define your POC goals: key metrics and two priority data domains.
Choose 5–10 representative prompts and two models to compare.
Build the POC using an existing tool (in my case, AWS’s solution) and run evaluations.
Review results with friendly teams and decide whether to scale the solution or build a custom version integrated into your internal prompt catalog.
I hope this helps.
Save this email. If you’re using AI agents, sooner or later, this info will come in handy.
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