Identifying Evaluation Metrics

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

Unlocking the Next Era of RAG System Evaluation: Insights from the Latest Comprehensive Survey Retrieval-Augmented Generation (RAG) has become a cornerstone for enhancing large language models (LLMs), especially when accuracy, timeliness, and factual grounding are critical. However, as RAG systems grow in complexity-integrating dense retrieval, multi-source knowledge, and advanced reasoning-the challenge of evaluating their true effectiveness has intensified. A recent survey from leading academic and industrial research organizations delivers the most exhaustive analysis yet of RAG evaluation in the LLM era. Here are the key technical takeaways: 1. Multi-Scale Evaluation Frameworks The survey dissects RAG evaluation into internal and external dimensions. Internal evaluation targets the core components-retrieval and generation-assessing not just their standalone performance but also their interactions. External evaluation addresses system-wide factors like safety, robustness, and efficiency, which are increasingly vital as RAG systems are deployed in real-world, high-stakes environments. 2. Technical Anatomy of RAG Systems Under the hood, a typical RAG pipeline is split into two main sections: - Retrieval: Involves document chunking, embedding generation, and sophisticated retrieval strategies (sparse, dense, hybrid, or graph-based). Preprocessing such as corpus construction and intent recognition is essential for optimizing retrieval relevance and comprehensiveness. - Generation: The LLM synthesizes retrieved knowledge, leveraging advanced prompt engineering and reasoning techniques to produce contextually faithful responses. Post-processing may include entity recognition or translation, depending on the use case. 3. Diverse and Evolving Evaluation Metrics The survey catalogues a wide array of metrics: - Traditional IR Metrics: Precision@K, Recall@K, F1, MRR, NDCG, MAP for retrieval quality. - NLG Metrics: Exact Match, ROUGE, BLEU, METEOR, BertScore, and Coverage for generation accuracy and semantic fidelity. - LLM-Based Metrics: Recent trends show a rise in LLM-as-judge approaches (e.g., RAGAS, Databricks Eval), semantic perplexity, key point recall, FactScore, and representation-based methods like GPTScore and ARES. These enable nuanced, context-aware evaluation that better aligns with real-world user expectations. 4. Safety, Robustness, and Efficiency The survey highlights specialized benchmarks and metrics for: - Safety: Evaluating robustness to adversarial attacks (e.g., knowledge poisoning, retrieval hijacking), factual consistency, privacy leakage, and fairness. - Efficiency: Measuring latency (time to first token, total response time), resource utilization, and cost-effectiveness-crucial for scalable deployment.

Cracking a GenAI Interview? Be Ready to Talk LLM Quality & Evaluation First If you’re walking into a GenAI interview at an enterprise, expect one theme to dominate: “How do you prove your LLM actually works, stays safe, and scales?” Here’s a practical checklist of evaluation areas you must be know for sure: 1. Core Model Evaluation • Accuracy, Exact Match, F1 for structured tasks. • Semantic similarity scores (BERTScore, cosine). • Distributional quality (MAUVE, perplexity). 2. Generation Quality & Faithfulness • Hallucination detection via NLI/entailment. • Groundedness in RAG with RAGAS metrics. • Multi-judge scoring: pairwise preference, rubric-based evaluation. 3. RAG & Contextual Systems • Retrieval metrics: Recall@k, MRR, nDCG. • Context efficiency: % of tokens in window that actually matter. • Hybrid retrieval performance (vector + keyword). 4. Alignment & Safety • RLHF limits and failure modes. • Safety tests: toxicity, jailbreak success rate, PII leakage. • Human-in-the-loop QA for high-risk cases. 5. Agentic & Multi-Step Workflows • Tool-use accuracy and recovery from errors. • Success rate in completing tasks end-to-end. • Multi-agent orchestration challenges (deadlocks, cost spirals). 6. LLMOps (Enterprise Grade) • Deployment: FastAPI + Docker + K8s with rollback safety. • Monitoring: hallucination rate, latency, prompt drift, knowledge drift. • Drift detection: prompt drift, data drift, behavioral drift, safety drift. • Continuous feedback: synthetic test sets + human eval loops. 7. MCP (Model Context Protocol) • Why interoperability across tools matters. • How to design fallbacks if an MCP tool fails mid-workflow. 🔑 Interview Tip: Don’t just name metrics. Be ready to explain why they matter in production: • How do you detect hallucination at scale? • What do you monitor beyond tokens/sec? • How do you know when your RAG pipeline is drifting? 👉 If you can answer these clearly, you’re not just “LLM-ready.” You’re enterprise-ready.

"𝗧𝗵𝗲 𝗺𝗲𝘁𝗿𝗶𝗰 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗴𝗼𝗮𝗹. 𝗪𝗲 𝗱𝗼 𝗻𝗼𝘁 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻; 𝘄𝗲 𝗰𝗮𝗿𝗲 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗺𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗴𝗼𝗮𝗹, 𝗼𝗿 𝗮𝘁 𝗹𝗲𝗮𝘀𝘁 𝘄𝗲 𝘀𝗵𝗼𝘂𝗹𝗱." The paper 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗮 𝗠𝗲𝗮𝘀𝘂𝗿𝗲? 𝗔 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗳𝗿𝗼𝗺 𝗟𝗮𝗿𝗴𝗲-𝗦𝗰𝗮𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 (𝗔𝗖𝗠 𝗦𝗜𝗚𝗜𝗥’𝟮𝟰) highlights the importance of metrics and outlines key considerations for selecting effective metrics in real-world scenarios. Although the focus is on information retrieval/search, the best practices can be applied to any domain. 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘀𝗵𝗼𝘂𝗹𝗱: 𝟭) 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝘃𝗮𝗹𝘂𝗲: metrics should measure something real which directly or indirectly ties into business value. Metrics should be stable – any variation should be due to change in observable behavior, not measurement noise. 𝟮) 𝗠𝗼𝘁𝗶𝘃𝗮𝘁𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: improving a metric should drive improvements to business value. Hence the sensitivity of a metric is important: it should be able to detect changes or effects accurately. 𝟯) 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲: A metric should be interpretable: it should be easy to communicate to stakeholders as well as help develop and debug a system. For e.g., “better search” is a vague concept. A well-defined metric forces agreement on what “better” is. There should be a correlation between system level/Data science metrics and business metrics. 𝗪𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗮 𝗺𝗲𝗮𝘀𝘂𝗿𝗲? 1) 𝗦𝗼𝗰𝗶𝗮𝗹 𝗮𝗻𝗱 𝗹𝗲𝗴𝗮𝗹: metrics should conform to privacy and ethical concerns – private data such as emails, health records or other personal profile data may be restricted. 2) 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: metrics should correlate with some business value and generalize to new situations. For e.g., a metric for evaluating search relevancy for web search engines should also work for other kinds of search engines such as ecommerce and healthcare. 3) 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: implementation and maintenance costs as well as time to run experiments. Scalability of industrial scale systems, computing and storage-resources of ongoing metrics computation. 4) 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆: metrics shouldn’t be negatively affected by data sampling strategies. For e.g., Metrics should be reliable for both frequent/head search queries as well as infrequent/tail search queries. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝗳𝗳𝗲𝗰𝘁𝘀:   1) 𝗕𝗲 𝘄𝗮𝗿𝘆 𝗼𝗳 𝗚𝗼𝗼𝗱𝗵𝗮𝗿𝘁’𝘀 𝗟𝗮𝘄: when a measure becomes a target, it becomes a bad measure. E.g To maximise clicks, some might promote clickbait.   2) 𝗧𝗲𝗻𝘀𝗶𝗼𝗻 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: different parts of an organization might be focusing on metrics which may conflict each other. For e.g., a focus on maximizing revenue in search may lead to more ads and lower quality. Paper link: https://lnkd.in/eK4gjSRV

Engineers keep asking me the same question about LLM testing. "How do I know if my evaluation metrics are actually working?" Most teams run evaluations, see a score, and assume it means something. But they never validate whether their metrics catch real failures. Last month, a Series B company came to us after their "95% accuracy" RAG system hallucinated customer data in production. Their evaluation pipeline gave them a false sense of security. The problem? They never tested their tests. Here's what we do at Confident AI to validate evaluation metrics: Test against known failures. Take 10 to 20 examples where you KNOW the LLM failed. If your metrics don't flag them, they're broken. Create adversarial test sets. Build datasets designed to break your system. Your metrics should catch edge cases and ambiguous queries. Compare against human judgment. Have domain experts label 50 random outputs. If your metrics agree less than 80% of the time, you have a metrics problem, not a model problem. The meta-lesson: evaluation is only valuable if you can trust your evaluation. What's your approach to validating metrics?

🤔 How Do You Actually Measure Learning That Matters? After analyzing hundreds of evaluation approaches through the Learnexus network of L&D experts, here's what actually works (and what just creates busywork). The Uncomfortable Truth: "Most training evaluations just measure completion, not competence," shares an L&D Director who transformed their measurement approach. Here's what actually shows impact: The Scenario-Based Framework "We stopped asking multiple choice questions and started presenting real situations," notes a Senior ID whose retention rates increased 60%. What Actually Works: → Decision-based assessments → Real-world application tasks → Progressive challenge levels → Performance simulations The Three-Point Check Strategy: "We measure three things: knowledge, application, and business impact." The Winning Formula: - Immediate comprehension - 30-day application check - 90-day impact review - Manager feedback loop The Behavior Change Tracker: "Traditional assessments told us what people knew. Our new approach shows us what they do differently." Key Components: → Pre/post behavior observations → Action learning projects → Peer feedback mechanisms → Performance analytics 🎯 Game-Changing Metrics: "Instead of training scores, we now track: - Problem-solving success rates - Reduced error rates - Time to competency - Support ticket reduction" From our conversations with thousands of L&D professionals, we've learned that meaningful evaluation isn't about perfect scores - it's about practical application. Practical Implementation: - Build real-world scenarios - Track behavioral changes - Measure business impact - Create feedback loops Expert Insight: "One client saved $700,000 annually in support costs because we measured the right things and could show exactly where training needed adjustment." #InstructionalDesign #CorporateTraining #LearningAndDevelopment #eLearning #LXDesign #TrainingDevelopment #LearningStrategy

The most underestimated part of building LLM applications? Evaluation. Evaluation can take up to 80% of your development time (because it’s HARD) Most people obsess over prompts. They tweak models. Tune embeddings. But when it’s time to test whether the whole system actually works? That’s where it breaks. Especially in agentic RAG systems - where you’re orchestrating retrieval, reasoning, memory, tools, and APIs into one seamless flow. Implementation might take a week. Evaluation takes longer. (And it’s what makes or breaks the product.) Let’s clear up a common confusion: 𝗟𝗟𝗠 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 ≠ 𝗥𝗔𝗚 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. LLM eval tests reasoning in isolation - useful, but incomplete. In production, your model isn’t reasoning in a vacuum. It’s pulling context from a vector DB, reacting to user input, and shaped by memory + tools. That’s why RAG evaluation takes a system-level view. It asks: Did this app respond correctly, given the user input and the retrieved context? Here’s how to break it down: 𝗦𝘁𝗲𝗽 𝟭: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹. → Are the retrieved docs relevant? Ranked correctly? → Use LLM judges to compute context precision and recall → If ranking matters, compute NDCG, MRR metrics → Visualize embeddings (e.g. UMAP) 𝗦𝘁𝗲𝗽 𝟮: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻. → Did the LLM ground its answer in the right info? → Use heuristics, LLM-as-a-judge, and contextual scoring. In practice, treat your app as a black box and log: - User query - Retrieved context - Model output - (Optional) Expected output This lets you debug the whole system, not just the model. 𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘴𝘢𝘮𝘱𝘭𝘦𝘴 𝘢𝘳𝘦 𝘦𝘯𝘰𝘶𝘨𝘩? 5–10? Too few. 30–50? Good start. 400+? Now you’re capturing real patterns and edge cases. Still, start with how many samples you have available, and keep expanding your evaluation split. It’s better to have an imperfect evaluation layer than nothing. Also track latency, cost, throughput, and business metrics (like conversion or retention). Some battle-tested tools: → RAGAS (retrieval-grounding alignment) → ARES (factual grounding) → Opik by Comet (end-to-end open-source eval + monitoring) → Langsmith, Langfuse, Phoenix (observability + tracing) TL;DR: Agentic systems are complex. Success = making evaluation part of your design from Day 0. We unpack this in full in Lesson 5 of the PhiloAgents course. 🔗 Check it out here: https://lnkd.in/dA465E_J

Uncomfortable Reality: You won't improve what you don't measure This goes for everything, Quality is no exception. Quality metrics serve as the foundation of operations, quality management and continuous improvement. Here are six essential quality metrics that you need to know to boost your business: 1/ Quality Rate ↳ % of products/services that meet quality standards ↳ High rate = effective processes, satisfied customers ↳ Low rate = improvement needed 2/ Defects Per Million Opportunities (DPMO) ↳ Similar to DPPM, but considers total opportunities for defects ↳ Allows organizations to assess processes holistically ↳ Helps target specific areas for improvement ↳ Comprehensive quality performance metric 3/ Rework Percentage ↳ Proportion of work that must be redone due to defects/errors ↳ High percentage signals process inefficiencies ↳ Important metric for cost reduction initiatives 4/ Process Capability ↳ Measures process within tolerances ↳ Helps organizations determine process consistency ↳ Important for customer satisfaction and management 5/ Defective Parts Per Million (DPPM) ↳ Quantifies the # of defective parts in a million produced ↳ Crucial for high volume operations ↳ Helps identify trends in defects 6/ Process Capability Index (Cpk) ↳ Takes process capability even further ↳ Helps center process performance ↳ helps decrease variability Become a great leader - measure to improve.

🎯 LLMs and agentic systems hold great potential, but effective evaluation is more important. In my research and current work, I’ve learned that strong evaluation strategies are essential for achieving real impact. Good evaluation is key, as high latency and poor results can seriously hurt the user experience. 🎯 Depending on usecase, some libraries that can help you in evaluation and I have explored during my work. -------------- 1. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 > FAISS (Facebook AI Similarity Search): It is mainly used for fast similarity search. - FAISS can help you evaluate retrieval efficiency in terms of speed and accuracy, especially for large datasets. 🔗 https://lnkd.in/dXSBdW5y > pyserini: Often used for dense and sparse retrieval evaluation in RAG setups. and built on top of Lucene and Anserini. - It is useful for evaluating retrieval quality by providing standard retrieval metrics like mean average precision (MAP) and nDCG. 🔗 https://lnkd.in/dPnJj3gv > ScaNN (Scalable Nearest Neighbors): Developed by Google. - ScaNN provides fast and efficient similarity search and is optimized for dense embeddings, helping with the retrieval speed evaluation. 🔗 https://lnkd.in/dh5t3BMR -------------- 2. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 > nlg-eval: This library provides several metrics for natural language generation evaluation, including BLEU, METEOR, and ROUGE. 🔗 https://lnkd.in/dm4H9iaP > BERTScore: It uses contextual embeddings from pre-trained models like BERT to evaluate the similarity between generated text and reference text. https://lnkd.in/d5Bv26vN > QuestEval: QuestEval is a learned metric that combines question answering with generation evaluation. - Assess if two different inputs contain the same information.  - It can deal with multimodal and multilingual inputs. 🔗 https://lnkd.in/dRZFY2np -------------- 3. 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐑𝐀𝐆 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 > Ragas: Specifically designed for RAG systems. - Evaluate both retrieval and generation components, - Offer a comprehensive overview of the entire RAG pipeline performance. 🔗 https://lnkd.in/d5yk686K > LangChain: Also provides evaluation tools, including quality assessment of both retrieval and generation in multi-step pipelines. > LlamaIndex for evaluating our RAG-based systems. - Offers evaluation modules. - Assess key aspects like correctness, faithfulness, and relevancy etc. > OpenAI Evals - It’s flexible - Allowing for both quantitative metrics and human evaluations, - Useful if you’re using OpenAI models. 🔗 https://lnkd.in/diDR2V6B > LangFair by CVS Health: - This new framework talks about Bring Your Own Prompt and assess bias and fairness in model outputs. 🔗 https://lnkd.in/dEYDB_k9 Image credit: https://lnkd.in/dCSJi-pB #rag #evaluation #llms #ml #ai

🔍 Beyond Accuracy: Diving Deeper into Model Evaluation In machine learning, there's so much more to a model's performance than accuracy alone. In the latest edition of The Data Science Decoder, I explore why evaluating your ML models requires a holistic approach—one that includes metrics like Precision, Recall, AUC-ROC, and Calibration. Understanding these metrics isn’t just technical jargon; it’s essential to achieving impactful, trustworthy results. Imagine using a model for fraud detection or healthcare predictions. Would you rely solely on accuracy? Probably not. This article breaks down when and why alternative metrics matter, providing you with a roadmap for more informed decision-making. 💡 Key takeaways include: - Real-life use cases where these metrics are critical - Visuals that help demystify complex evaluation techniques - Insights into how a well-rounded approach can transform your outcomes Whether you’re aiming for higher impact or building a more resilient model, these insights are for you. Check out the full article and elevate your model evaluation strategy! #DataScience #MachineLearning #AI