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Selecting the right AI foundation model is a critical decision for enterprises integrating artificial intelligence into their workflows. Whether the goal is customer support automation, content generation or specialized applications such as legal analysis or medical diagnostics, AI large language models (LLMs) must align with business objectives while ensuring accuracy, efficiency and compliance.
With an increasing number of foundation models — each with distinct architectures, training methodologies and cost structures — businesses need a systematic approach to evaluation. A well-chosen AI model can enhance productivity, streamline decision-making and reduce operational costs. Conversely, selecting an ill-suited model can lead to inefficiencies, compliance risks and wasted investment.
This guide provides a high-level overview of the most relevant techniques, performance metrics and industry best practices for selecting the right AI model. It also provides key resources to consider during this process.
Table of Contents
- What Is AI Model Evaluation?
- Types of AI Model Evaluations
- Other Key Considerations for Evaluating AI Models
- Evolving Benchmarks: Lessons From DeepSeek
What Is AI Model Evaluation?
Unlike traditional software testing, where outputs are deterministic, large language models generate probabilistic responses. The same input may yield different results based on model architecture, training data and prompt variations. As a result, AI model evaluation is a systematic process for evaluating how well an AI model performs on a specific set of tasks.
Early AI evaluations, such as the Turing Test, relied on subjective assessments, using human intuition to judge machine-generated responses.
However, as LLM models have become more effective, evaluation has become increasingly automated and metrics-based. Accuracy is a primary metric, alongside efficiency, reliability, fairness and alignment with business or research objectives.
Just as AI models change, organizational needs do too. For example, if a model is only effective in English, it may become less valuable as an organization expands globally and considers different factors such as bias, hallucinations and scalability. As a result, AI evaluation is an ongoing, iterative process, not a one-time event.
Related Article: Tech's Ethical Test: Building AI That's Fair for All
Types of AI Model Evaluations
AI models are assessed using manual (human-driven) and automated (algorithmic) evaluation techniques. Each method serves a specific role, depending on the task's complexity, the need for precision and the scale at which AI is being deployed.
Manual Evaluations
Manual evaluations use human expertise to assess AI-generated responses based on accuracy, coherence and relevance. While they provide deep insights, they can be time-consuming and complex to scale without structured methods.
Examples of these methods include:
- Exploratory Testing ("Vibe Checks"): Quick, informal assessments to catch significant errors early in the process.
- Expert Review: Industry professionals assess outputs for accuracy and compliance, essential in regulated fields.
- Structured Annotation: Evaluators rate responses using predefined criteria to reduce subjectivity.
According to researchers, human evaluators can identify and intercept biases in AI outputs that automated systems might overlook. Plus, human-centered evaluation can determine if model explanations are comprehensible and trustworthy to end-users.
Generally, manual evaluations are used for early-stage validation before scaling AI deployment.
Automated Evaluations
Automated evaluations use algorithms to assess AI performance against predefined metrics, offering scalable and objective analysis. These evaluations fall into two main categories: reference-based and non-reference-based.
Reference-Based Evaluations
Reference-based evaluations compare AI-generated responses to a known correct answer ("ground truth"). They are most effective for structured tasks with verifiable outputs, such as summarization, translation and fact-based Q&As.
Key examples include:
- MMLU (Massive Multitask Language Understanding): Assesses a model's performance across 57 diverse subjects using multiple-choice questions.
- ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Measures the overlap between AI-generated text and reference summaries, commonly used for summarization tasks.
- BLEU (Bilingual Evaluation Understudy): Evaluates the similarity between machine-generated translations and human translations, focusing on translation fluency and precision.
- HELM (Holistic Evaluation of Language Models): Provides a comprehensive evaluation of language models across various scenarios, focusing on metrics such as accuracy, robustness, fairness and efficiency.
Reference-based evaluations are most effective at identifying a model’s ability for applications requiring high accuracy and automated knowledge retrieval.
Non-Reference-Based Evaluations
Non-reference-based evaluations assess AI-generated outputs based on qualitative attributes rather than a fixed "correct" answer. These methods are essential for AI applications involving creative writing, chatbot interactions or recommendation systems that must adhere to an organizational brand or vision.
Key metrics include:
- Semantic Similarity: Measures whether AI-generated responses convey the same meaning as a human-written response, even if phrasing differs.
- Bias & Toxicity Analysis: Identifies unintended biases related to gender, ethnicity or political perspectives to ensure ethical AI deployment.
- Fluency & Grammar Checks: Assesses syntax, coherence and readability to ensure AI outputs meet professional writing standards.
- Mathematical and Logical Accuracy: This evaluates whether models correctly handle problem-solving and reasoning tasks, which is critical for AI in finance, science and technical domains.
LLM-as-a-Judge Evaluations
A growing approach in AI evaluation is using one AI model to assess the output of another. This method is beneficial when large-scale human review is impractical.
A standard method involves providing the “judging” model with two responses and having it select the better one, similar to the industry tool Chatbot Arena, which gives humans similar evaluation abilities.
One shortcoming of this method, according to researchers, is that LLMs are limited by their inability to appropriately weigh the importance of various topics, often overemphasizing minor details and undervaluing critical information. However, you can improve LLM outputs with strategic prompt engineering that explicitly outlines how to prioritize relevant information.
Other Key Considerations for Evaluating AI Models
Beyond technical evaluation, leaders must consider operational, financial and strategic factors to ensure seamless integration with existing systems, scalability and adherence to regulatory and ethical standards.
Training Data Transparency
Training data determines the effectiveness and relevance of an AI model. Many proprietary models do not disclose their datasets, making it challenging to assess their suitability for specific industries, especially in regulated fields like finance, healthcare and law. However, third-party tools like the Data Provenance Initiative have database evaluations for 1800+ datasets to train LLM models.
Whenever possible, consider answering these questions when evaluating training data:
- Does the model’s training dataset include domain-specific terminology and context relevant to my industry?
- Has the model been fine-tuned using proprietary or specialized datasets to enhance accuracy in my field?
- Is there a risk of bias, outdated information or low-quality data affecting model reliability?
- How does the provider handle data privacy and compliance with regulations such as GDPR, HIPAA or financial industry standards?
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Latency & Performance
Response time is as critical as accuracy for real-time AI applications such as customer service chatbots, algorithmic trading or fraud detection. Some AI models deliver high-quality outputs but introduce latency due to complex computations. Monitoring observability with tools like OpenLLMetry can provide ongoing insight into performance questions.
Pricing Models & Cost Efficiency
The cost of AI implementation varies based on licensing, API access and infrastructure requirements. A model that appears cost-effective at low usage levels can become expensive as usage scales. However, decomposing complex tasks into more straightforward ones can make offloading steps to an older model easier and reduce costs.
Token Windows & Context Length
Token limits determine how much text a model can process in a single query. If your AI application requires analyzing long documents, maintaining conversation memory or synthesizing multiple sources, token windows become a key factor. Token windows have expanded significantly, from the early 4K token windows of ChatGPT 3.5 to Gemini 2.0 Pro's two million token context window.
Long token windows can significantly impact model evaluation, making add-on systems such as retrieval-augmented generation (RAG) less necessary in specific contexts. RAG is most effective for tasks requiring deep expertise and for real-time decision-making. It’s also a method that can reduce hallucinations and increase the reliability of outputs.
Related Article: Single Vendor vs. Best of Breed: Which Data Stack Model Works Best?
Evolving Benchmarks: Lessons From DeepSeek
The emergence of models like DeepSeek-R1 underscores the complexity of AI evaluation, revealing how performance can vary widely across different tasks and metrics.
DeepSeek-R1 has demonstrated strong reasoning capabilities, reportedly matching OpenAI’s high-end o1 model on several benchmarks — all at a fraction of the cost. However, while automated evaluations highlight its strengths, they also expose its limitations.
Manual comparative analyses can still favor OpenAI’s o1 model, reinforcing that AI performance is often "in the eye of the beholder." Human interpretation plays a crucial role in assessing an AI model’s real-world effectiveness, and factors like usability, nuance and context can significantly affect model selection.
This variability highlights a critical challenge in AI evaluation: models can be fine-tuned to excel on specific metrics, sometimes “gaming” the system to achieve high benchmark scores. For example, a model optimized for the MMLU benchmark — based on multiple-choice questions — may not perform as well in open-ended reasoning tasks. The importance of benchmarks for a model’s adoption is hotly contested and often sparks controversy, such as that surrounding OpenAI funding the organization responsible for benchmarking its latest model, o3.
AI evaluation is as much an art as it is a science. Organizations must take a holistic approach to ensure AI models align with business objectives — blending technical benchmarks with human-driven insights. A well-rounded evaluation strategy helps maximize AI’s potential while mitigating risks.
