In professional environments, an increasing number of companies are integrating Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) solutions to automate specific tasks or support their teams. However, ensuring the accuracy and relevance of these systems’ responses is critical, particularly when handling sensitive data or high-stakes business decisions.

Evaluating these models requires a rigorous approach, ensuring:

• Thorough verification and validation of responses to prevent errors or misleading information.
• Adaptation to industry-specific vocabulary, which can often be complex.
• Compliance with company policies, including confidentiality requirements and stylistic guidelines.

Two Approaches of Evaluation: Human and Automatic

Human evaluation

It can be approached in two ways:

Evaluation with a “gold” reference

• A predefined test set exists, where each question is paired with a reference answer. The evaluator compares the LLM’s (or RAG’s) response to this “gold” standard, assessing similarity, relevance, and linguistic quality.
• This method is effective but requires domain expertise and a well-structured, representative set of question-answer pairs.
• It is time-consuming and may be influenced by cognitive biases, such as confirmation bias or personal preferences.

Evaluation by end-users

• Feedback is collected directly from users in real-world scenarios.
• This approach provides large-scale, practical insights based on actual usage.
• However, it can be difficult to structure and may be skewed by non-expert feedback, as users may have varying levels of knowledge.

Automatic Evaluation

Automated evaluation seeks to minimize reliance on human judgment by partially or fully automating the assessment of response quality. This approach helps reduce subjectivity, ensuring more consistent evaluations while significantly accelerating testing and validation processes. By leveraging predefined metrics and algorithms, automated evaluation enables large-scale, repeatable assessments, making it an essential tool for efficiently monitoring and improving model performance.

As AI models become more prevalent in business and research, the necessity of automated evaluation is becoming increasingly clear. This approach offers several key advantages:

• Streamlining testing processes by reducing the constant reliance on expert human reviewers.
• Eliminating human biases, which persist even when strict evaluation guidelines are in place.
• Ensuring consistency and comparability, allowing for reproducible assessments over time.
• Enabling continuous performance monitoring, automatically re-evaluating the model whenever new documents are integrated or updates are made.

By adopting automated evaluation, organizations can maintain high-quality AI outputs while improving efficiency, scalability, and reliability in their assessment workflows.

Scientific and Technological Perspective

Traditional NLP Evaluation Methods

Historically, Natural Language Processing (NLP) has relied on standardized metrics such as BLEU, ROUGE, and BERTScore to assess the quality of machine translation, text summarization, and other language-based tasks. These metrics focus on word overlap, semantic similarity, or embedding-based comparisons to determine how closely a generated response aligns with a predefined reference.

However, when applied to retrieval-augmented generation (RAG) systems or large language models (LLM) in specialized business domains, these traditional evaluation methods quickly reveal their limitations:

• They fail to fully capture the business-specific context, industry jargon, or domain-specific reasoning.
• They often require a predefined reference answer, which becomes impractical when multiple valid responses exist.
• They do not assess the coherence, factual accuracy, or contextual appropriateness of generated responses beyond surface-level similarity.

Emerging Evaluation Frameworks: RAGAS, DeepEval, Auto-Evaluator, and Beyond

To address these shortcomings, new evaluation frameworks have been developed, specifically designed for RAG-based AI systems:

• RAGAS, DeepEval: These tools extend traditional evaluation by incorporating both information retrieval quality and response generation accuracy, allowing a more granular assessment of each step in the RAG pipeline.

While these advancements represent a step forward, they still pose several challenges:

• Limited adaptation to industry-specific requirements – They often overlook the nuances of specialized business processes and terminologies.
• Lack of holistic evaluation – Unlike human experts, they struggle to assess nuanced correctness, logical consistency, or the overall impact of a response.
• Challenges in policy integration – Businesses need evaluation mechanisms that align with internal guidelines, such as ensuring compliance with confidentiality policies or filtering sensitive information.

Forthcoming Perspectives: Paving the Way for Human-Like AI Evaluation

In the subsequent publication, we will delve deeper into emerging approaches that bridge the gap between automated metrics and a more user-centric, “human-like” evaluation framework. We will explore novel criteria-based methods, operational user mapping, and discuss how these advancements can be practically applied—from prompt engineering to project management. Stay engaged for an in-depth look at how contextual and adaptive AI evaluation stands to reshape the field and deliver tangible impact.