This text is customized from a lecture sequence I gave at Deeplearn 2025: From Prototype to Manufacturing: Analysis Methods for Agentic Applications¹.
, which measure an AI system’s efficiency in use-case-specific, real-world settings, are underadopted and understudied. There may be nonetheless an outsized focus in AI literature on basis mannequin benchmarks. Benchmarks are important for advancing analysis and evaluating broad, common capabilities, however they hardly ever translate cleanly into task-specific efficiency.
In contrast, task-based evaluations allow us to measure how programs carry out on the merchandise and options we really wish to ship, they usually allow us to do it at scale. With out that, there’s no strategy to know if a system is aligned with our expectations, and no strategy to construct the belief that drives adoption. Evaluations are how we make AI accountable. They’re not only for debugging or QA; they’re the connective tissue between prototypes and manufacturing programs that individuals can depend on.
This text focuses on the why — why task-based evaluations matter, how they’re helpful all through the event lifecycle, and why they’re distinct from AI benchmarks.
Evaluations Construct Belief
When you may measure what you might be talking about, and categorical it in numbers, one thing about it; however if you can not measure it, … your information is of a meager and unsatisfactory sort.
Lord Kelvin
Evaluations outline what “good” seems to be like for a system. With out them, there’s no accountability — simply outputs with nothing however vibes to guage whether or not they meet the mark. With evaluations, we will create a construction for accountability and a path to enchancment. That construction is what builds belief, in order that we will:
- Outline applicable habits so groups agree on what success means.
- Create accountability by making it doable to check whether or not the system meets these requirements.
- Drive adoption by giving customers, builders, and regulators confidence that the system behaves as meant.
Every cycle of analysis and refinement strengthens that belief, turning experimental prototypes into programs folks can depend upon.
Evaluations Help the Whole Lifecycle
Evaluations aren’t restricted to a single stage of growth. They supply worth throughout the whole lifecycle of an AI system:
- Debugging and growth: catching points early and guiding iteration.
- Product validation and QA: confirming that options operate correctly beneath real-world situations.
- Security and regulatory technique: assembly requirements that demand clear, auditable proof.
- Person belief: demonstrating reliability to the individuals who work together with the system.
- Steady enchancment: creating the inspiration for fine-tuning and steady coaching/deployment loops, so programs evolve alongside new information.
In every of those phases, evaluations act because the hyperlink between intention and final result. They be sure that what groups got down to construct is what customers really expertise.
Benchmarks vs. Job-Particular Evaluations
Benchmarks dominate a lot of the AI literature. They’re broad, public, and standardized, which makes them priceless for analysis. They permit straightforward comparability throughout fashions and assist drive progress in basis mannequin capabilities. Datasets like MMLU or HELM have change into reference factors for measuring common efficiency.
However benchmarks include limits. They’re static, sluggish to evolve, and deliberately troublesome to assist separate cutting-edge mannequin efficiency, however not at all times in ways in which precisely replicate real-world duties. They danger encouraging leaderboard chasing moderately than product alignment, they usually hardly ever inform you how a system will carry out within the messy context of an precise software.
Contemplate the next thought train: if a brand new basis mannequin performs a couple of proportion factors higher on a benchmark or a leaderboard, is that sufficient to justify refactoring your manufacturing system? What about 10%? And what in case your current setup already performs effectively with quicker, cheaper, or smaller fashions?
Job-based evaluations serve a special function. They’re particular, typically proprietary, and tailor-made to the necessities of a specific use case. As a substitute of measuring broad functionality, they measure whether or not a system performs effectively for the merchandise and options being constructed. Job-based evals are designed to:
- Help the complete lifecycle — from growth to validation to post-market monitoring.
- Evolve as each the system and the product mature.
- Be sure that what issues to the top person is what will get measured.
Benchmarks and task-based evaluations aren’t in competitors. Benchmarks transfer the analysis frontier, however task-based evals are what make merchandise work, construct belief, and finally drive adoption of AI options.
Closing Ideas
Evaluations aren’t simply overhead. They outline what success seems to be like, create accountability, and supply the inspiration for belief. Benchmarks have their place in advancing analysis, however task-based evaluations are what flip prototypes into manufacturing programs.
They help the complete lifecycle, evolve with the product, and allow measuring alignment at scale. Most significantly, they be sure that what will get constructed is what customers really need.
This primary piece has targeted on the “why.” Within the subsequent article, I’ll flip to the “how” — the sensible techniques for evaluating agentic AI programs, from easy assertions and heuristics to LLM judges and real-world suggestions.
The views expressed inside are my private opinions and don’t symbolize the opinions of any organizations, their associates, or staff.
[1] M. Derdzinski, From Prototype to Manufacturing: Analysis Methods for Agentic Functions (2025), DeepLearn 2025
