Getting started with Large Language Model evaluation doesn’t have to feel overwhelming. Whether you’re developing a chatbot for customer service or implementing an AI writing assistant, understanding how to properly evaluate your model is the difference between a successful deployment and a frustrating experience for your users. The real challenge isn’t just knowing what metrics and benchmarks exist, but understanding which ones actually matter for your specific project.​

Understanding the Foundation: Metrics and Benchmarks

The first thing to grasp is the difference between metrics and benchmarks, because people often mix these up. A metric is simply a measurement that tells you something specific about your model’s performance. Think of it like checking your car’s speedometer or fuel gauge. Each metric gives you one piece of information. Meanwhile, a benchmark is more like taking your car through a complete inspection that tests everything from the engine to the brakes. It uses multiple metrics together to give you a comprehensive picture of how well your model performs compared to others.​

When you’re evaluating an LLM, you’re essentially asking two questions. First, how well does it perform specific tasks? That’s where metrics come in. Second, how does it compare to other models? That’s where benchmarks shine. You need both perspectives to make informed decisions about your model.​

Core Evaluation Metrics

1. Perplexity

Best for: Language modeling, text generation, model training evaluation

Perplexity sounds complicated but really isn’t. Imagine you’re trying to predict what word comes next in a sentence. If you’re very confident, you might only consider two or three possibilities. If you’re completely lost, you might be choosing from hundreds of words. Perplexity measures this uncertainty. A model with a perplexity of 20 is about as confused as if it were choosing randomly from 20 equally likely words. Lower numbers mean your model is more confident and generally more accurate.​

When to use it: This metric shines during model training and fine-tuning. If you’re adapting a base model to write medical reports or legal documents, tracking perplexity helps you see if the model is getting better at predicting domain-specific language. It’s also valuable when comparing different versions of the same model or deciding between similar architectures.

Real-world example: If you’re building an autocomplete feature for a writing app, perplexity tells you how well your model predicts the next word. A model with perplexity of 15 will give better suggestions than one with perplexity of 50.

2. Accuracy

Best for: Classification tasks, question answering with definite answers, multiple choice problems

For tasks where you need specific correct answers, accuracy tells you the percentage of times your model gets things right overall. It’s the most straightforward metric we have. If your model correctly answers 85 out of 100 questions, you have 85% accuracy. While simple, accuracy can sometimes be misleading for creative or open-ended tasks where “correctness” isn’t black and white.​

When to use it: Accuracy works perfectly when there’s a clear right or wrong answer. Think sentiment analysis (positive/negative/neutral), email categorization (spam/not spam), or multiple choice questions. However, avoid relying on accuracy alone for imbalanced datasets where one category vastly outnumbers others.

Real-world example: For a content moderation system that needs to flag inappropriate posts, accuracy tells you what percentage of decisions were correct. But be careful, if 95% of posts are appropriate, a lazy model that marks everything as “appropriate” gets 95% accuracy while missing all the problematic content.

3. Precision and Recall

Best for: Information retrieval, classification with imbalanced classes, safety-critical applications

Precision tells you how often your model is correct when it thinks it found something important. If your model identifies 10 emails as spam and 8 actually are spam, that’s 80% precision. Recall tells you how many of the important things your model actually found. If there were 20 actual spam emails and your model found 15 of them, that’s 75% recall. These metrics matter when you need to balance catching everything important against avoiding false alarms.​

When to use it: Use precision when false positives are costly. A medical diagnostic system flagging healthy patients as sick wastes resources and causes anxiety. Use recall when missing something is dangerous. A fraud detection system must catch as many fraudulent transactions as possible, even if it means investigating some legitimate ones.

Real-world example: For a resume screening system, high precision means most candidates you interview are qualified, but you might miss good candidates (low recall). High recall means you catch all qualified candidates, but you’ll waste time interviewing unqualified ones (low precision). Your business needs determine which matters more.

4. F1 Score

Best for: Balancing precision and recall, comparing models on imbalanced datasets, single metric comparisons

The F1 score cleverly combines precision and recall into a single number between 0 and 1, with higher being better. It’s particularly useful when you can’t afford to miss important information but also can’t have too many false positives. An F1 score of 0.8 means your model has found a good balance between precision and recall.​

When to use it: F1 score is your go-to metric when both false positives and false negatives carry roughly equal costs. It’s especially valuable for imbalanced datasets where accuracy becomes misleading. Use it when you need one number to compare multiple models quickly.

Real-world example: For a job matching platform that connects candidates with positions, you want to avoid both recommending unsuitable jobs (frustrating candidates) and missing perfect matches (losing opportunities). F1 score tells you if your system balances both concerns effectively.

5. Cross-Entropy Loss

Best for: Training monitoring, probability calibration, comparing model confidence

This metric quantifies how far off your model’s predictions are from the actual correct answers. It essentially compares what the model thinks should happen with what actually should happen. Lower cross-entropy means your model’s predictions are closer to reality. During training, models work to minimize this metric, making it fundamental for understanding how well your model is learning.​

When to use it: Cross-entropy is most valuable during model training and development. It helps you understand not just whether predictions are right, but how confident the model is. This matters for applications where you need calibrated probabilities, like risk assessment systems that need to say “I’m 70% confident” accurately.

Real-world example: For a weather forecasting system powered by LLMs, cross-entropy helps ensure that when your model says there’s a 30% chance of rain, it actually rains about 30% of the time in those situations. This calibration builds user trust.

Text Quality Metrics

1. BLEU Score

Best for: Machine translation, tasks with definite correct answers, technical documentation generation

The BLEU score compares generated text to reference text by looking for matching phrases. Originally designed for translation, it counts how many words or phrases in your model’s output appear in the reference answer. Scores range from 0 to 1, with values close to 1 indicating highly accurate output. A BLEU score of 0.3 to 0.7 suggests moderate accuracy, while values near 0 indicate poor quality. This metric works best when exact wording matters, like in translation or technical documentation.​

When to use it: BLEU shines when there’s a “correct” way to express something. Translation between languages, converting code comments to documentation, or generating product descriptions from specifications all benefit from BLEU scoring. However, avoid it for creative tasks where many valid responses exist.

Real-world example: If you’re building a system that translates product descriptions from English to Spanish, BLEU tells you how closely your translations match professional human translations. A score of 0.5 or higher indicates your system produces translations that capture most of the meaning and phrasing.

2. ROUGE Score

Best for: Text summarization, content compression, information extraction

ROUGE is perfect for evaluating summaries because it checks whether the important content from the original text appears in your generated summary. It measures recall rather than precision, asking “Did we capture all the important stuff?” rather than “Is everything we wrote correct?” This makes it ideal for tasks where completeness matters more than brevity.​

When to use it: Use ROUGE whenever you’re condensing information. Summarizing news articles, creating executive summaries of reports, or extracting key points from meeting transcripts all need ROUGE evaluation. It ensures your summaries don’t miss critical information.

Real-world example: For an app that summarizes research papers for busy professionals, ROUGE-1 tells you how many important individual words you captured, while ROUGE-L measures whether you preserved the logical flow of key concepts. High ROUGE scores mean users get the full picture without reading the entire paper.

3. BERTScore

Best for: Paraphrasing tasks, creative content, semantic similarity evaluation

Unlike BLEU and ROUGE, which look for exact word matches, BERTScore understands that “dog” and “canine” mean the same thing. It uses advanced language understanding to compare meanings rather than just surface-level words. This makes it much better at evaluating paraphrased or creatively written content where the exact wording varies but the meaning stays the same.​

When to use it: BERTScore excels when semantic equivalence matters more than exact wording. Content rewriting, paraphrasing tools, question answering where multiple phrasings are correct, and creative writing assistance all benefit from this metric.

Real-world example: For a content rewriting tool that helps students rephrase academic sources, BERTScore recognizes that “The experiment yielded positive results” and “The test produced favorable outcomes” convey the same meaning. Traditional metrics would score this poorly despite the semantic equivalence.

4. G-Eval

Best for: Complex content evaluation, multi-dimensional quality assessment, when human judgment is needed at scale

G-Eval takes evaluation to another level by using LLMs themselves to judge output quality. It generates a comprehensive assessment by having a powerful model evaluate responses across multiple dimensions like coherence, fluency, and relevance. This approach combines the scalability of automated metrics with judgment that’s closer to human evaluation.​

When to use it: G-Eval works brilliantly when you need nuanced evaluation that considers multiple quality dimensions simultaneously. Customer support responses, creative writing, educational content, and marketing copy all benefit from this holistic assessment approach.

Real-world example: For a customer service chatbot, G-Eval can simultaneously assess whether responses are helpful, empathetic, professional, and accurate. Instead of juggling four separate metrics, you get a comprehensive quality score that reflects overall response appropriateness.

LLM-Specific Metric

1. Answer Relevancy
   

Best for: Question answering systems, chatbots, search applications

This metric checks whether your model’s response actually addresses what the user asked. You’d be surprised how often models generate beautiful, coherent text that completely misses the point of the question. For customer service applications, irrelevant answers frustrate users and waste time, making this metric absolutely critical.​

When to use it: Answer relevancy is non-negotiable for any application where users ask questions and expect relevant responses. Customer support chatbots, educational tutors, search engines, and virtual assistants all need high answer relevancy.

Real-world example: If someone asks your travel chatbot “What’s the weather like in Paris in July?” and it responds with information about Paris landmarks, the text might be accurate and well-written, but it’s completely irrelevant. Answer relevancy would score this response poorly, alerting you to the problem.

2. Hallucination Detection

Best for: Factual content generation, RAG systems, information-critical applications

Models sometimes confidently state things that simply aren’t true. Hallucination detection identifies when your model makes things up. If you’re building anything that provides factual information, whether it’s medical advice or financial guidance, you absolutely must track how often your model invents information. The consequences of unchecked hallucinations can range from embarrassing to dangerous.​

When to use it: Hallucination detection is essential for any application where factual accuracy matters. Medical information systems, legal research tools, financial advisors, news summarization, and educational platforms cannot tolerate made-up information.

Real-world example: A medical information chatbot that invents drug interactions or contradicts established treatments could literally endanger lives. Hallucination detection would flag responses like “Taking aspirin with ibuprofen enhances effectiveness” when no such evidence exists, preventing dangerous misinformation from reaching users

3. Toxicity

Best for: User-facing applications, content generation, conversational AI

For any user-facing application, toxicity detection is non-negotiable. This metric flags harmful or offensive content in your model’s outputs. Even the best models can occasionally generate inappropriate content. Regular monitoring protects both your users and your reputation. This isn’t just about obvious profanity; it includes subtle biases and potentially harmful advice.​

When to use it: Every single user-facing application needs toxicity monitoring. Social media content generators, chatbots, writing assistants, and customer service systems must all ensure they never produce harmful, offensive, or discriminatory content.

Real-world example: For a creative writing assistant used in schools, toxicity detection catches and blocks suggestions containing violence, hate speech, or age-inappropriate content. Even if a student’s prompt inadvertently triggers inappropriate completions, the system prevents harmful content from reaching young users.

4. Tool Correctness

Best for: AI agents, multi-tool systems, workflow automation

If your LLM acts as an agent that can use tools or APIs, this metric determines whether it calls the right tools for the right tasks. A model that tries to use a calculator for translation tasks or a weather API for math problems needs improvement in tool selection.​

When to use it: Tool correctness matters for any AI agent that can access multiple tools or APIs. Personal assistants that can check calendars, send emails, and search the web need to choose the right tool for each request. Workflow automation systems that integrate multiple services absolutely require accurate tool selection.

Real-world example: For an AI assistant that helps with data analysis, tool correctness ensures that when a user asks “What’s the average sales figure?”, the system uses a calculation tool rather than a web search API. Using the wrong tool wastes time and produces nonsensical results.

5. Contextual Relevancy

Best for: RAG systems, document-based QA, knowledge retrieval applications

For Retrieval Augmented Generation (RAG) systems, this measures whether the information your system retrieves actually helps answer the user’s question. There’s no point having a brilliant language model if it’s working with irrelevant information. High contextual relevancy means your retrieval system is finding the right documents.​

When to use it: Contextual relevancy is critical for any system that retrieves information before generating responses. Legal document analysis, technical support systems that search knowledge bases, research assistants, and enterprise search all depend on retrieving relevant information.

Real-world example: For a legal research assistant, if a lawyer asks about “patent infringement precedents in software cases,” the system needs to retrieve relevant case law, not general patent information or software licensing guides. Contextual relevancy measures whether your retrieval system finds the right needles in the legal haystack.

Performance Metrics

1. Latency

Best for: Real-time applications, conversational AI, interactive systems

Latency measures how long your model takes to generate a response. For real-time applications like chatbots or voice assistants, users expect quick responses. High latency leads to poor user experience, no matter how good your model’s answers are. Track both average latency and worst-case response times.​

When to use it: Latency matters most for interactive applications where users are actively waiting for responses. Live chatbots, voice assistants, real-time translation, autocomplete features, and interactive tutoring systems all need consistently low latency.

Real-world example: For a customer service chatbot, if responses take 10 seconds to appear, users assume the system is broken and close the window, even if the eventual responses would be perfect. Latency monitoring helps you maintain response times under 2–3 seconds, keeping users engaged.

2. Coherence

Best for: Long-form content generation, multi-turn conversations, storytelling

This evaluates whether your generated text flows logically and maintains consistency throughout. A coherent response doesn’t contradict itself, maintains the same tone, and follows a logical structure. This is especially important for longer outputs like articles or detailed explanations.​

When to use it: Coherence is essential for any application generating extended text. Blog post writers, story generators, report creators, essay assistants, and conversation systems all need high coherence to produce usable output.

Real-world example: For an AI that generates marketing blog posts, coherence ensures the introduction’s promises are fulfilled in the body, the tone stays professional throughout, and the conclusion logically follows from the arguments presented. Without coherence monitoring, you might publish posts that contradict themselves or randomly shift topics.

3. Diversity

Best for: Creative content generation, recommendation systems, conversation variety

Diversity measures the variety and uniqueness of your model’s responses. A model that always gives the same answer to similar questions lacks diversity. This metric involves analyzing how different your responses are from each other, both in wording and content. Higher diversity often leads to more engaging user experiences.​

When to use it: Diversity matters for creative applications where repetition feels robotic. Story generators, conversational AI, content suggestion systems, and creative writing assistants all benefit from diverse outputs that keep users engaged.

Real-world example: For a chatbot that greets customers, a model with low diversity might say “Hello, how can I help?” to every user. A diverse model varies greetings (“Welcome! What brings you here today?”, “Hi there! Looking for something specific?”) making interactions feel more natural and less scripted.

Combined Evaluation Metrics

1. Groundedness and Correctness
   

Best for: RAG systems requiring factual accuracy, research assistants, information verification

When used together, these metrics reveal interesting patterns. High groundedness (0.9) with low correctness (0.4) means your system references the right sources but draws wrong conclusions. For example, a response stating “Einstein developed quantum mechanics” based on a context that mentions both Einstein and quantum mechanics separately. The response is grounded in the source material but factually incorrect.​

When to use it: This combination is invaluable for RAG systems where you need to understand both whether the model uses your documents (groundedness) and whether it interprets them correctly (correctness). Research assistants, legal document analysis, and medical information systems all need both metrics.

Real-world example: For a financial analysis tool that reads earnings reports, groundedness ensures the model references actual numbers from the reports, while correctness verifies it calculates profit margins accurately. A model might cite the right revenue and cost figures (high groundedness) but incorrectly compute the margin (low correctness).

2. Utilization and Completeness

Best for: Document retrieval optimization, comprehensive information gathering, search quality

These metrics together evaluate retrieval effectiveness. High utilization (0.9) with low completeness (0.3) indicates your system retrieves accurate but incomplete information. When asked about World War II causes, it might perfectly retrieve information about the invasion of Poland but miss other crucial factors. This suggests you might need to retrieve more chunks or improve your ranking strategy.​

When to use it: This pairing helps optimize RAG systems for comprehensiveness. Academic research assistants, technical documentation search, and competitive intelligence tools all need complete information, not just accurate fragments.

Real-world example: For a medical diagnosis support system, high utilization with low completeness means the system finds relevant symptoms but misses important ones. It might retrieve information about chest pain and shortness of breath but miss the critical detail about recent travel, leading to incomplete diagnostic support.

Key Benchmarks to Know

1. MMLU (Massive Multitask Language Understanding)
   

Best for: General-purpose models, assessing broad knowledge, academic applications

MMLU tests knowledge across 57 different subjects, from elementary math to advanced law. It’s like giving your model a comprehensive exam covering virtually every academic field. When someone says a model scores well on MMLU, they’re telling you it has broad, general knowledge that spans multiple domains. This benchmark has become the go-to test for assessing whether a model has genuinely broad capabilities.​

When to use it: Use MMLU when you need a general-purpose model that handles diverse topics. Educational platforms, research assistants, general chatbots, and knowledge management systems all benefit from models with strong MMLU performance.

Real-world application: If you’re building a homework help platform that needs to assist with everything from biology to literature to calculus, MMLU scores tell you whether your model has the breadth of knowledge students need. A model scoring 70% on MMLU can handle undergraduate-level questions across most subjects.

2. HumanEval

Best for: Code generation tools, programming assistants, developer productivity

HumanEval focuses specifically on code generation through programming problems. It presents coding challenges and checks if the generated code actually works by running it against test cases. If your application involves any programming tasks, this benchmark tells you whether your model can write functional, correct code. A high HumanEval score means the model doesn’t just write code that looks right; it writes code that actually runs.​

When to use it: HumanEval is essential for any coding-related application. Code completion tools, programming tutors, automated testing generators, and developer assistants all need strong HumanEval performance.

Real-world application: For a coding assistant like GitHub Copilot, HumanEval performance directly predicts how often the suggested code will actually work on the first try. A model scoring 60% on HumanEval generates working code more than half the time, significantly boosting developer productivity.

3. TruthfulQA

Best for: Factual information systems, educational tools, reducing misinformation

This benchmark specifically tests whether models will repeat common misconceptions or generate false but plausible-sounding answers. It contains questions designed to trip up models into stating popular falsehoods. For applications where factual accuracy matters, like educational tools or information systems, TruthfulQA performance is a critical indicator of reliability.​

When to use it: TruthfulQA matters for any application where spreading misinformation could cause harm. Health information systems, news summarization, educational platforms, and fact-checking tools all need models that resist generating popular but false information.

Real-world application: For a health information chatbot, TruthfulQA performance indicates whether the system will correctly reject common medical myths. A model that scores well won’t tell users that “cracking knuckles causes arthritis” or that “you need eight glasses of water daily” despite these being popular misconceptions.

4. HellaSwag
   

Best for: Conversational AI, common sense applications, natural interaction

Despite its quirky name, HellaSwag tests something fundamental: common sense reasoning. It presents the beginning of a scenario and asks the model to predict what happens next. These questions are trivially easy for humans (who score around 95% accuracy) but surprisingly challenging for models. Poor HellaSwag performance reveals gaps in understanding everyday situations and cause-effect relationships.​

When to use it: HellaSwag is valuable for applications requiring natural, human-like reasoning. Conversational assistants, story generators, game NPCs, and social robots all need the common sense that HellaSwag measures.

Real-world application: For a smart home assistant, HellaSwag performance predicts whether it will understand context naturally. When you say “I’m heading out for a run,” a model with good HellaSwag scores understands you might want weather information and a door unlocked, not a recipe for dinner.

5. ARC (AI2 Reasoning Challenge)
   

Best for: Educational applications, scientific reasoning, logical problem-solving

The ARC benchmark features grade-school science questions that test reasoning abilities. It includes both an “Easy” set for basic understanding and a “Challenge” set requiring deeper scientific reasoning. Questions demand not just factual recall but the ability to apply knowledge to novel situations, making it excellent for assessing genuine understanding versus memorization.​

When to use it: ARC is perfect for educational applications and any system requiring logical reasoning. Science tutors, exam prep tools, and reasoning-based games all benefit from models with strong ARC performance.

Real-world application: For an interactive science learning app, ARC-Challenge scores indicate whether your model can help students think through problems rather than just memorizing facts. A model that scores well can explain why objects fall at the same rate regardless of weight, not just state that they do.

6. Winogrande
   

Best for: Natural language understanding, context-aware systems, ambiguity resolution

Winogrande evaluates commonsense reasoning through pronoun resolution tasks. With 44,000 carefully designed problems, it tests whether models can understand context and common sense well enough to figure out what pronouns refer to in ambiguous sentences. This seemingly simple task reveals a lot about a model’s grasp of real-world knowledge.​

When to use it: Winogrande matters for applications requiring subtle language understanding. Document summarization, instruction following, conversational AI, and text analysis tools all need the contextual understanding that Winogrande measures.

Real-world application: For a meeting summarization tool, Winogrande performance predicts whether it correctly identifies who said what and who made which commitments. In “The manager told the contractor that he needed to review the plans,” strong Winogrande performance helps identify whether “he” refers to the manager or contractor based on context.

7. GSM8K
   

Best for: Mathematical reasoning, word problem solving, quantitative applications

GSM8K contains 8,500 grade school math word problems that require multiple steps of logical thinking. These aren’t just arithmetic; they’re word problems that need understanding, planning, and calculation. Performance on GSM8K indicates whether a model can handle basic quantitative reasoning that humans use in everyday life.​​

When to use it: GSM8K is essential for applications involving calculations or quantitative reasoning. Financial calculators, shopping assistants, educational math tools, and business analytics systems all need strong GSM8K performance.

Real-world application: For a personal finance chatbot that helps users budget, GSM8K scores predict whether it can solve problems like “If you earn $3,000 monthly, spend $800 on rent, $400 on food, and want to save 20%, how much can you spend on entertainment?” Strong GSM8K performance means accurate financial guidance.

8. MT-Bench
   

Best for: Conversational AI, multi-turn dialogue, chatbot development

MT-Bench stands out by testing conversational abilities across multiple turns. With 80 questions generating 3,300 responses, it evaluates whether models can maintain coherent, informative conversations. It uses an innovative “LLM-as-a-judge” approach where strong models assess response quality, making it particularly relevant for chatbot applications.​

When to use it: MT-Bench is crucial for any multi-turn conversational application. Customer service chatbots, therapy bots, tutoring systems, and virtual companions all need strong MT-Bench performance to maintain engaging, coherent conversations.

Real-world application: For a mental health support chatbot, MT-Bench performance indicates whether the system can follow conversation threads, remember previous statements, and provide contextually appropriate responses across a 10-minute conversation without losing track or contradicting itself.

9. SuperGLUE
   

Best for: Advanced language understanding, comprehensive NLP evaluation, research applications

SuperGLUE represents the evolution of benchmark difficulty. When models began achieving near-human performance on the original GLUE benchmark, SuperGLUE was created with more challenging tasks requiring deeper reasoning. It includes diverse formats from coreference resolution to question-answering, pushing models to demonstrate genuine language understanding.​

When to use it: SuperGLUE is valuable for applications requiring sophisticated language understanding. Advanced document analysis, complex question answering, and nuanced text classification all benefit from models with strong SuperGLUE performance.

Real-world application: For a legal contract analysis system that needs to identify subtle obligations and conditions, SuperGLUE performance indicates whether the model can handle the complex reasoning required. It’s not enough to find keywords; the system must understand implied meanings and logical relationships.

10. BIG-Bench
    

Best for: Research, emergent capability testing, pushing model limits

BIG-Bench features 214 tasks designed to probe emergent capabilities in large models. This massive collection aims to identify abilities that appear only at certain scales and pushes the boundaries of what we consider possible with language models. It’s particularly useful for understanding what new capabilities emerge as models grow larger.​

When to use it: BIG-Bench is primarily for research and development teams exploring model capabilities. It helps identify what your model can do beyond standard tasks, revealing unexpected strengths that might open new application possibilities.

Real-world application: For a research team deciding whether to deploy a new larger model, BIG-Bench reveals emergent abilities like analogical reasoning or complex pattern recognition that weren’t explicitly trained but emerged from scale. This helps justify the increased computational cost if new capabilities enable valuable applications.

Choosing the Right Metrics and Benchmarks for Your Project
Now that you understand what each metric and benchmark measures, here’s how to choose the right ones for common application types.

For Customer Service Chatbots: Focus on answer relevancy, toxicity, coherence, and latency. Evaluate using MT-Bench for conversational ability and HellaSwag for common sense. Track F1 score if you’re classifying customer issues.

For Content Generation Tools: Prioritize coherence, diversity, and BERTScore for semantic quality. Use BLEU or ROUGE if you’re adapting existing content. Monitor toxicity and latency for user-facing applications.

For Code Assistants: HumanEval is your primary benchmark. Track accuracy for code completion and latency for interactive features. Tool correctness matters if your system integrates with development environments.

For Educational Platforms: Use MMLU for broad knowledge, ARC for reasoning, and GSM8K for math. Track answer relevancy and hallucination detection to ensure accurate information. TruthfulQA helps prevent spreading misconceptions.

For RAG-Based Systems: Contextual relevancy and hallucination detection are critical. Monitor groundedness with correctness, and utilization with completeness. Answer relevancy ensures you’re actually helping users.

For Translation Services: BLEU score is your primary metric. Track latency for real-time translation and diversity to avoid repetitive phrasings.

For Summarization Tools: ROUGE scores tell you if you’re capturing key information. Monitor coherence for readability and latency for user experience.

Practical Implementation Strategy
Start simple and build complexity as you learn what matters for your specific users. Begin with three to five metrics that directly impact user satisfaction. Add benchmarks relevant to your domain. Create a small test set from real user interactions, and gradually expand it as you encounter edge cases.

Monitor your chosen metrics continuously in production. Set up alerts for when critical metrics decline. Run benchmark comparisons monthly to track improvements. Most importantly, combine automated metrics with regular manual review. Numbers tell you what’s happening; human judgment tells you why it matters.

The goal isn’t perfect scores on every metric and benchmark. The goal is building something users find valuable and reliable. Use metrics and benchmarks as tools to understand your system’s strengths and weaknesses, then improve the aspects that matter most for your specific application. That’s how evaluation transforms from an academic exercise into a practical tool for building better LLM applications.