The world of AI is moving at a breakneck pace. New large language models (LLMs) like GPT-4, Claude 3, and Llama 3 are released constantly, each claiming superior performance. Public leaderboards and standard benchmarks like SQuAD or CNN/DailyMail are excellent for getting a general sense of a model's capabilities. But they tell you only part of the story.
When it comes to your specific business problem, generic benchmarks can be misleading. A model that excels at summarizing news articles might completely fail at summarizing legal contracts or medical records. The true test of an AI model's value isn't its score on a public leaderboard—it's how it performs on your data, for your use case.
This guide explores why custom benchmarking with your private datasets is critical and how you can implement it to make smarter, data-driven decisions.
Standardized datasets are the foundation of AI research, providing a common ground for comparing models. However, relying on them exclusively for business applications has significant drawbacks:
Choosing an AI model based solely on generic scores is like hiring a chef based on their ability to win a chili cook-off when you need them to run a French pastry shop. You need to test them in the right kitchen with the right ingredients.
Using your own private data for performance testing moves you from generic comparisons to specific, actionable insights. This is where you gain a true competitive advantage.
Setting up a custom evaluation workflow can seem daunting, but breaking it down into steps makes it manageable. Here’s how you can do it with a platform like benchmarks.do.
First, clearly identify what you want the model to do. Is it question-answering, text summarization, data extraction, or classification? Then, define what "good" looks like. For summarization, you might use ROUGE scores. For Q&A, you might look at Exact Match and F1-Score.
Create a high-quality, representative sample of your data. This "golden set" should consist of:
The quality of your benchmark is only as good as the quality of this dataset.
This is where the process often becomes a bottleneck. Building a reliable evaluation pipeline to run multiple models against your dataset involves handling different APIs, managing rate limits, parsing various outputs, and calculating scores.
This is the problem Benchmarks.do was built to solve. Our platform allows you to quantify AI performance, instantly. You can securely provide your custom dataset and test any number of models through a single, simple API call.
Instead of building complex infrastructure, you get a clean, comparable result, like this:
{
"benchmarkId": "bm_1a2b3c4d5e",
"name": "LLM Performance Comparison",
"status": "completed",
"completedAt": "2023-10-27T10:30:00Z",
"results": [
{
"task": "text-summarization",
"dataset": "cnn-dailymail",
"scores": [
{ "model": "gpt-4", "rouge-1": 0.45, "rouge-2": 0.22, "rouge-l": 0.41 },
{ "model": "claude-3-opus", "rouge-1": 0.47, "rouge-2": 0.24, "rouge-l": 0.43 },
{ "model": "llama-3-70b", "rouge-1": 0.46, "rouge-2": 0.23, "rouge-l": 0.42 }
]
},
{
"task": "question-answering",
"dataset": "squad-v2",
"scores": [
{ "model": "gpt-4", "exact-match": 88.5, "f1-score": 91.2 },
{ "model": "claude-3-opus", "exact-match": 89.1, "f1-score": 91.8 },
{ "model": "llama-3-70b", "exact-match": 88.7, "f1-score": 91.5 }
]
}
]
}
The results from your custom benchmark are your source of truth. In the example above, while all three models perform closely, Claude 3 Opus shows a slight edge in both summarization and Q&A tasks. On your custom data, these small margins can translate into significant differences in user experience and operational efficiency.
Use these insights to select your champion model, fine-tune a runner-up, or decide that a different approach is needed. AI evaluation is not a one-time event but a continuous cycle of testing, learning, and optimizing.
AI model benchmarking is the process of systematically evaluating and comparing the performance of different artificial intelligence models on standardized tasks and datasets. This helps in understanding their capabilities, limitations, and suitability for specific applications.
Standardization is crucial because it ensures a fair, 'apples-to-apples' comparison. By using the same datasets, metrics, and evaluation environments, Benchmarks.do provides reliable and reproducible results, removing variability so you can make decisions with confidence.
Benchmarks.do supports a wide variety of models, including Large Language Models (LLMs), computer vision models, recommendation engines, and more. Our platform is designed to be extensible for diverse AI domains and architectures.
Yes, our platform is flexible. While we provide a suite of industry-standard datasets for common tasks, you can also securely upload and use your own proprietary datasets to benchmark model performance on tasks specific to your business needs.
Stop relying on generic hype. The secret to successfully deploying AI is to rigorously test models in the context where they will be used. By benchmarking with your own proprietary data, you can move past the public leaderboards and find the model that delivers real, measurable value for your unique challenges.
Ready to get started? Visit Benchmarks.do to run your first custom AI benchmark and make data-driven decisions today.