# Setting up your own benchmark: a practical step-by-step plan

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# Setting up your own benchmark: a practical step-by-step plan for AI evaluation

 By Ivo Donker - 6 August 2026

 

 
 In the fast-evolving world of large language models (LLMs), public benchmarks are springing up like mushrooms. Although leaderboards such as MMLU or GSM8K give a general impression of a model's capabilities, they often fall short when assessing company-specific applications. Public tests test general knowledge, but not how well an AI model searches your internal documentation, handles complaints according to your house style, or analyzes legal contracts.

 To guarantee quality, cost efficiency, and reliability in production, setting up your own domain-specific benchmark is essential. In this article, we walk through a structured step-by-step plan for building an evaluation framework that seamlessly fits your organization and objectives.

 
## Why generic benchmarks are not enough

 General benchmarks face two major challenges: contamination and relevance. Because many public test sets have become part of the training data of the latest LLMs, high scores often give a distorted picture. In addition, a high score on math puzzles says little about the performance of an AI agent that has to translate customer questions into actions via an API.

 Your own benchmark provides direct insight into questions such as:

 
 
- Is a smaller (and cheaper) open-source model sufficient for the specific task?
 
- Has an update to the prompt or RAG pipeline improved or worsened answer quality?
 
- How does the system perform on rare edge cases?
 

 
## The 6-step plan for your own benchmark

 
### Step 1: Determine the evaluation goal and constraints

 Before collecting data, it must be clear exactly what you want to measure. Is it about the factual accuracy of an answer, consistency of formatting (such as JSON output), latency (speed), or cost per processed task?

 Also define the tolerance threshold: for an internal assistant, a small error may be acceptable, while for medical or legal analyses the margin of error must be zero. Clarifying these boundaries determines how you set up the evaluation metrics in later steps.

 
### Step 2: Compile a representative test dataset

 A benchmark stands or falls with the quality of the test set. A good test dataset consists of at least 100 to 500 varied examples that reflect actual practice.

 Preferably draw your examples from production environments, historical customer questions, or real business information. Ensure a good balance when compiling the data:

 
 
- 80% Standard cases: Common, everyday tasks.
 
- 15% Exceptions: More complex questions with multiple conditions or nuance.
 
- 5% Adversarial / edge cases: Questions deliberately designed to be ambiguous or to try to mislead the model.
 

 It is crucial to prevent data leakage during this process. See the article on [setting up a test set without data leaks](/en/testset-zonder-datalek) to ensure the evaluation data remains separate from any training data.

 
### Step 3: Choose the right evaluation metrics

 Not every task can be assessed in the same way. Distinguish between deterministic and non-deterministic metrics.

 
 
 
 
 Task type | 
 Recommended metric | 
 Explanation | 
 

 
 
 
 
- Structured data (JSON/SQL)
 
- Exact Match, Parsing Success Rate
 
- Measures whether the output can be directly processed by software.
 

 
 
- Information extraction
 
- Precision, Recall, F1-score
 
- Compares the extracted entities with the desired values.
 

 
 
- Knowledge questions / RAG
 
- Faithfulness, Answer Relevance
 
- Tests whether the answer is correct and grounded in the source.
 

 
 
- Open text generation
 
- LLM-as-a-Judge, human review
 
- Assesses style, tone, conciseness, and logical structure.
 

 
 
 

 
### Step 4: Set up the evaluation method

 You have three options for carrying out the measurements, which in practice are often combined:

 
 
- Rule-based evaluation (code): Use Python scripts to perform regex matches, length checks, or JSON validation. This is extremely fast and cheap.
 
- Model-based evaluation (LLM-as-a-Judge): Deploy a powerful model (such as Claude 3.5 Sonnet or GPT-4o) to assess the generated answers against a clear rubric. Read more about implementing this on the page on [LLM-as-a-Judge](/en/llm-as-a-judge).
 
- Human evaluation (human-in-the-loop): Have domain experts perform spot-check assessments to verify the validity of your automated evaluation.
 

 If your application uses external sources to answer questions, combine these steps with a specific [RAG evaluation](/en/rag-evaluatie) to test both the search results (retrieval) and the processing (generation) separately.

 
### Step 5: Automation and CI/CD integration

 A benchmark only delivers real value once it is run continuously. Include the benchmark in your development process (CI/CD pipeline). As soon as a developer adjusts a prompt, changes the system instructions, or switches to a new model version, the benchmark should run automatically.

 # Voorbeeld van een eenvoudige evaluatieloop in Python
def run_benchmark(test_set, system_under_test, judge_model):
 results = []
 for item in test_set:
 # 1. Genereer antwoord van het te testen systeem
 response = system_under_test.generate(item['prompt'])
 
 # 2. Evalueer het resultaat met een Judge-model
 score = judge_model.evaluate(
 input_prompt=item['prompt'],
 expected=item['ground_truth'],
 actual=response
 )
 results.append({"id": item['id'], "score": score})
 return results

 
### Step 6: Analyze, report, and iterate

 Store all results in a structured way. Don't just look at the average score, but specifically at the downward outliers. Which specific questions cause errors? Is it a missing source, a misinterpretation of the prompt, or a limitation of the chosen LLM?

 Based on these insights, adjust your system and add the newly discovered failure cases to your test dataset. This way, your benchmark grows along with the maturity of your application.

 
## Pitfalls to avoid

 
 
- Starting the benchmark too large: Start with 50 to 100 high-quality examples. A small, sharp dataset is better than a large, messy one.
 
- Relying on a single score: An average score of 85% can hide the fact that a critical part of the application fails 50% of the time. Breaking it down by category is essential.
 
- Not calibrating the 'judge': If you use an LLM as an assessor, regularly check whether the AI's assessments match the judgments of human experts within your team.
 

 
## Building further on your AI infrastructure

 Setting up your own benchmark is the first step toward professional and predictable AI management. Want to brainstorm about the architecture of your evaluation framework, or looking for support in setting up automated test pipelines? Check out the options at [llmnet consultancy](https://consultancy.llmnet.nl/en/) or join the discussions on the [llmnet community](https://community.llmnet.nl/en/).

 

 
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