This article is changed from a series of talks I provided DEEPLEALNN 2025: From Prototype to production: Agentic Applications Techniques.
According to the performance of AI program in the relevant, actual land use arrangements, they are less than less than not to worry. There is still special focus on AI literature at the Foundation Model Benchmark. The benches are essential for improvement in research and comparing broad, familiar skills, but rarely translates work-specific.
In contrast, let's work-based tests let's balance how the systems make the products and features we want to free, and enable us to do it on a scale. Apart from this, there is no way to know if the program is in line with what is expected, and there is no way to create a pull-up. Assessment is the way we make AI respond. Not only a Debugging or QA; It is the tissue involved among the production programs and programs to produce.
This document focuses on why – Why are there a situation of work based on work, how useful for all life progress, and why they differed from Ai beni.
The test creates trust
When you can measure what you are talking about, and it reveals it with numbers, you know something about; But if you can measure, … your information is a little and satisfied type.
King Kalvin
Checking explains which “good” seems to be a plan. Without them, no accountability – just go out vibes judgment even if they met Mark. By examining, we can create a defense and development method. That building is for us who build trust, so that we can:
- Describe proper behavior So teams agree about what it means to succeed.
- Create accountability By making the exercise that the program meets those standards.
- Driving By providing users, developers, and regulators to confess that the system is behaved as targeted.
Each of the checkup and reflection cycles enables the trust, to convert test prototypes into programs to visit.
Checking supports all health
Assessment is not limited to one development stage. They give value throughout Ai System Program:
- Adjusting Error and Development: Holding issues early and direction.
- Product Verification and QA: To ensure that effective features under the original world conditions.
- Security and Control strategy: Meeting levels are looking for clear, legitimate evidence.
- User trust: Disloyalty to honesty for people contacting the program.
- Continuous Development: Building the foundation for good training and continuing the training / delivery of goods, so systems from the side of the new data.
In each of these categories, Avolastion AS as a connection between purpose and effect. They ensure which groups established to build is what users actually experience.
Benchmarks VS test.
The benches of most AI books. They are broad, in the community, and stand, making it advise by research. They allow simple comparison of models that fall across the models and help drive developments in the base model skills. Datasets like MMLU or Helm into a standard performance rating.
But benchmarks come with restrictions. They are static, slow to appear, and are difficult to help distinguish the performance of a cutting model, but not always in ways that show the real world activities. They risked the advanced encouraging to rush into productivity rather than the product align, and they rarely tell you how the system will make the process of the actual application.
Consider the following exercise test: If a new model of a few percentage of the better percentage of bench or in the main range, are you sufficient to process your production system? What about 10%? And what if your existing setup is already effective with a quick, cheap, or smaller model?
The job-based assessment helps a different purpose. They are clear, often corrected, and they are in line with the requirements of a particular case. Instead of estimating the powerful force, they measure the plan for the efficiency of the products and features. The work-based reasons are intended for:
- Support the perfect health – from development to the monitoring of the market on the back of the market.
- Convert as system and product rang.
- Make sure that what is important to the last user is what is measured.
The benchmarks and work-based tests are not a competition. The benches offer the research boundary, but already based on the activities, forms products, form trust, and eventually conducts the acceptance of AI.
Thoughts of closing
The test is not just above. They explain what success is, create accountability, and give the foundation of trust. The benches have their area in the development of research, but work-based assessment is the changing prototypes into production programs.
They base the perfect lifeclecle, repent of product, and they enable measurable measurements on a scale. Most importantly, they ensure that the construction of what users actually need.
This first piece focuses on “Why.” In the next article, I am going to turn to “the active way – the active tactics of evaluating AI, from simple thinking and Heursistics to achieve the LLM judges and the real world response.
The comments that have been expressed within my private ideas and is not represented the opinions of any organizations, partners, or employees.
[1] M. DedDzinski, From Prototype to Production: Agentic Apps of Apps(2025), deEpleplearn 2025
