Prompt Management, Tracing, and Evals: The New Table Stakes for GenAI
Building AI products used to be about finding the right model. Now it's about operational excellence.
As AI moves from experiments to production, a new infrastructure layer has become essential: prompt management, tracing, and evaluations. These aren't optional additions—they're table stakes for running reliable systems.
The Operational Gap
When AI was a research project, developers could iterate manually. Tweak a prompt, test manually, move on.
Production changes everything. You need:
- Reproducible experiments
- Cost tracking
- Debugging capabilities across models, tools, and data stores
Without these, you're flying blind.
What Prompt Management Solves
Prompts are code. They deserve the same treatment:
Version control tracks how prompts change over time and why
A/B testing compares different prompt strategies objectively
Centralization prevents scattered, undocumented prompt changes across teams
The blind spots are costly: runaway token costs, brittle prompt handling, and failed experiments that can't be reproduced.
The Tracing Imperative
LLM interactions are opaque. A single request might involve multiple model calls, tool invocations, and data fetches.
Tracing breaks this open:
- Step-by-step visibility into what the model did
- Cost attribution per operation
- Root cause analysis when things go wrong
OpenLit's OTEL-first approach converts opaque model interactions into structured traces. This debugging capability becomes essential when systems scale.
The Evals Foundation
How do you know if your AI is actually working?
Evals provide the answer. They're systematic evaluations that measure:
- Task completion rates
- Quality scores
- Regression detection
Without evals, you're guessing. With them, you can ship with confidence and catch regressions before users see them.
The Vendor-Neutral Standard
One key insight: standards matter.
OPAMP and similar vendor-neutral protocols reduce lock-in while enabling collector management. You're not trapped with one provider, but you get consistent instrumentation.
This matters as much as the tools themselves. Infrastructure that works across providers gives you flexibility.
The Takeaway
The AI infrastructure layer is maturing. Prompt management, tracing, and evals aren't glamorous—but they're essential.
Companies that build on solid operational foundations can iterate faster and with more confidence. Those that skip this layer will struggle with reliability, cost overruns, and debugging nightmares.
The lesson: invest in operations early, or pay later.
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