Why model evaluation choices impact user experience

Here is why leaderboards can fool you (and what to do instead) 👇 Benchmarks are macro averages, and your application is a micro reality. A model that’s top-3 on MMLU or GSM-Plus might still bomb when asked to summarize legal contracts, extract SKUs from receipts, or answer domain-specific FAQs. That’s because: 👉 Benchmarks skew toward academic tasks and short-form inputs. Most prod systems run multi-turn, tool-calling, or retrieval workflows the benchmark never sees. 👉 Scores are single-shot snapshots. They don’t cover latency, cost, or robustness to adversarial prompts. 👉 The “average of many tasks” hides mode failures. A 2-point gain in translation might mask a 20-point drop in structured JSON extraction. In short, public leaderboards tell you which model is good in general, not which model is good for you . 𝗕𝘂𝗶𝗹𝗱 𝗲𝘃𝗮𝗹𝘀 𝘁𝗵𝗮𝘁 𝗺𝗶𝗿𝗿𝗼𝗿 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗰𝗸 1️⃣ Trace the user journey. Map the critical steps (retrieve, route, generate, format). 2️⃣ Define success per step. Example metrics: → Retrieval → document relevance (binary). → Generation → faithfulness (factual / hallucinated). → Function calls → tool-choice accuracy (correct / incorrect). 3️⃣ Craft a golden dataset. 20-100 edge-case examples that stress real parameters (long docs, unicode, tricky entities). 4️⃣ Pick a cheap, categorical judge. “Correct/Incorrect” beats 1-5 scores for clarity and stability 5️⃣ Automate in CI/CD and prod. Gate PRs on offline evals; stream online evals for drift detection. 6️⃣ Iterate relentlessly. False negatives become new test rows; evaluator templates get tightened; costs drop as you fine-tune a smaller judge. When you evaluate the system, not just the model, you’ll know exactly which upgrade, prompt tweak, or retrieval change pushes the real-world metric that matters: user success. How are you’re tailoring evals for your own LLM pipeline? Always up to swap notes on use-case-driven benchmarking Image Courtesy: Arize AI ---------- Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and resources!

In the last 90 days I spoke to 12 CXO.  They all said one thing: GenAI doesn't deliver business value. The reason? It’s not because of model choice. Not because of bad prompts. But because they skip the most important part: LLM evaluation This is why evals matter. In one Datali project, testing took us from 60% to 92% accuracy. Not by luck and blind trying. But by building a rigorous, automated testing pipeline. Here’s the boring but harsh truth: You don’t write a perfect system prompt and test it. You write tests first and discover prompts that pass them. This what you get: 1//  You gain crystal clear visibility - the perfect picture of what works and what doesn’t. You see how your system behaves across real-world inputs. You know where failures happen and why. You can plan risk mitigation strategies early 2// You iterate faster. Once you're testing thoroughly, you can run more experiments, track their results and revisit what worked best. Even months later. You catch problems early. You refine prompts, add data or fine-tune with confidence. You iterate faster from PoC → MVP → production, adjusting to user feedback without guesswork. 3// You build better products in less time. The better means here: Higher accuracy → less hallucination, better task handling. More stability → no surprises in production, fewer user complaints. 4// You reach the desired business impact: ROI, KPIs and cost savings. This is the combined result of previous actions. They drive your KPIs. If your system is accurate, stable and aligned to the user’s goals - that’s everything you need. Shorter development cycles = faster time to market Fewer bugs = lower support costs Focused iterations = less wasted dev time It’s priceless. But you can get it only with the right approach.

Traditional usability tests often treat user experience factors in isolation, as if different factors like usability, trust, and satisfaction are independent of each other. But in reality, they are deeply interconnected. By analyzing each factor separately, we miss the big picture - how these elements interact and shape user behavior. This is where Structural Equation Modeling (SEM) can be incredibly helpful. Instead of looking at single data points, SEM maps out the relationships between key UX variables, showing how they influence each other. It helps UX teams move beyond surface-level insights and truly understand what drives engagement. For example, usability might directly impact trust, which in turn boosts satisfaction and leads to higher engagement. Traditional methods might capture these factors separately, but SEM reveals the full story by quantifying their connections. SEM also enhances predictive modeling. By integrating techniques like Artificial Neural Networks (ANN), it helps forecast how users will react to design changes before they are implemented. Instead of relying on intuition, teams can test different scenarios and choose the most effective approach. Another advantage is mediation and moderation analysis. UX researchers often know that certain factors influence engagement, but SEM explains how and why. Does trust increase retention, or is it satisfaction that plays the bigger role? These insights help prioritize what really matters. Finally, SEM combined with Necessary Condition Analysis (NCA) identifies UX elements that are absolutely essential for engagement. This ensures that teams focus resources on factors that truly move the needle rather than making small, isolated tweaks with minimal impact.

Don't just blindly use LLMs, evaluate them to see if they fit into your criteria. Not all LLMs are created equal. Here’s how to measure whether they’re right for your use case👇 Evaluating LLMs is critical to assess their performance, reliability, and suitability for specific tasks. Without evaluation, it would be impossible to determine whether a model generates coherent, relevant, or factually correct outputs, particularly in applications like translation, summarization, or question-answering. Evaluation ensures models align with human expectations, avoid biases, and improve iteratively. Different metrics cater to distinct aspects of model performance:  Perplexity quantifies how well a model predicts a sequence (lower scores indicate better familiarity with the data), making it useful for gauging fluency. ROUGE-1 measures unigram (single-word) overlap between model outputs and references, ideal for tasks like summarization where content overlap matters. BLEU focuses on n-gram precision (e.g., exact phrase matches), commonly used in machine translation to assess accuracy. METEOR extends this by incorporating synonyms, paraphrases, and stemming, offering a more flexible semantic evaluation. Exact Match (EM) is the strictest metric, requiring verbatim alignment with the reference, often used in closed-domain tasks like factual QA where precision is paramount. Each metric reflects a trade-off: EM prioritizes literal correctness, while ROUGE and BLEU balance precision with recall. METEOR and Perplexity accommodate linguistic diversity, rewarding semantic coherence over exact replication. Choosing the right metric depends on the task—e.g., EM for factual accuracy in trivia, ROUGE for summarization breadth, and Perplexity for generative fluency. Collectively, these metrics provide a multifaceted view of LLM capabilities, enabling developers to refine models, mitigate errors, and align outputs with user needs. The table’s examples, such as EM scoring 0 for paraphrased answers, highlight how minor phrasing changes impact scores, underscoring the importance of context-aware metric selection. Know more about how to evaluate LLMs: https://lnkd.in/gfPBxrWc Here is my complete in-depth guide on evaluating LLMs: https://lnkd.in/gjWt9jRu Follow me on my YouTube channel so you don't miss any AI topic: https://lnkd.in/gMCpfMKh

Evaluations are extremely important for any AI application. That is, how do you know which models to use, if things are working optimally, etc? Today, we’re sharing a bit about our eval stack.   Behind every Decagon AI agent is a rigorous model evaluation engine built for the highest-stakes customer interactions.   When your agents are handling complex, customer-facing use cases, you need more than just promising model outputs. You need a framework that continuously and precisely measures real performance at scale.   In our latest blog post, we break down the core components of that evaluation framework: 🧠 LLM-as-judge evaluation – scoring real-world interactions across relevance, correctness, empathy, and naturalness, with human validation to catch edge cases 📊 Ground truth benchmarking – using curated, expert-labeled datasets to measure factuality and intent coverage 🚦 Live A/B testing – deploying variants in production and measuring their impact on real business outcomes like CSAT and resolution rate   This evaluation doesn’t stop once the latest version of an AI agent ships. Every insight feeds back into prompts, retrieval, and agent logic. The result: continuous improvement in the quality of customer experiences.   Check out the full blog in the comments.

Are we benchmarking LLMs the wrong way? Why we need more LLM arenas. Benchmarks are essential for evaluating the performance of Large Language Models (LLMs). But are they capturing what really matters—the user experience? A new study suggests that we might be missing the mark. Researchers collected 1,863 real-world use cases from 712 participants across 23 countries. They call this the User Reported Scenarios (URS) dataset. Using URS, they benchmarked 10 LLM services on their ability to satisfy user needs across 7 different intent categories. The results are showing that the benchmark scores aligned well with user-reported experiences, highlighting a critical oversight in current evaluation practices: subjective scenarios. The study proposes a paradigm shift in how we evaluate LLMs, moving from predefined abilities to a user-centric perspective. By benchmarking LLMs based on authentic, diverse user needs, we can ensure that these powerful tools are truly serving their intended purpose—collaborating with and assisting users in the real world. ↓ Liked this post? Follow the link under my name and never miss a paper highlight again 💡

Evaluating LLM Systems in Production: Lessons from Andrei Lopatenko  What Andrei Lopatenko breaks down brilliantly:  - Validate changes: Are they improving the product?   - Support launches: Can the model handle real-world tasks?   - Drive continuous improvements: Metrics guide development, like test-driven development in software.  Evaluating LLMs in production is nothing like traditional ML evaluation.   Why? Because LLMs:  → Handle multiple tasks (e.g., summarization, embeddings, Q&A).   → Operate in diverse semantic areas.   → Pose unique risks like hallucinations and emerging behaviors.  Key Differences From Classical ML Evaluation:  1. Many Metrics, Many Use Cases     Unlike ML models with a single focus, LLMs need metrics tailored for each task. Think embedding accuracy, reasoning depth, or response coherence.   2. Higher Complexity and Risk     LLM outputs directly impact user experiences, increasing stakes. New types of errors require creative evaluation techniques.   3. Continuous Process     Evaluating LLMs isn’t a one-and-done deal. Every update requires iteration.  How to Evaluate Effectively:   - Task-Specific Metrics: For example, BERTScore for text similarity or RAGAs for retrieval-augmented generation.   - End-to-End Evaluation: Consider production metrics like latency, cost, and user engagement alongside NLP benchmarks.   - Embedding Benchmarks: Use frameworks like MTEB for clustering, retrieval, and classification.   - User-Centric Metrics: Measure engagement, response time, or sentiment to align with business needs.  Tools to Simplify the Process:  - Open-source harnesses like EleutherAI and RAGAs.   - Continuous evaluation frameworks (e.g., ARES for domain-specific tasks).   - Automated pipelines to test and deploy rapidly without bottlenecks.  The Challenges:  - Subjectivity: Textual outputs often lack clear “right” answers.   - Domain-Specific Needs: Metrics must be tailored to the unique context of your LLM applications.   - Cost and Speed: Evaluation should be scalable and efficient to avoid delays in iteration cycles.   --- I share my learning journey here. Join me and let's grow together.  Enjoy this? Repost it to your network and follow Karn Singh for more.  

From my conversations, I'm noticing different trends between how AI engineers deploy evaluation models (model as a judge) in production monitoring vs application testing. 💡 Value - Monitoring: provides real time alerts of performance or used to preemptively block problematic outputs from surfacing to the user -Testing: regression testing applications to establish baseline performance before pushing to deployment ⚡ Speed - Monitoring: near real time, requires extremely fast token processing - Testing: batch, async, often processing large test sets, lower token processing speed required 🎯 Accuracy - Monitoring: overindex on false positives, better to flag something as problematic and be wrong than to let something slip between the cracks - Testing: high correlation with human graders, be as accurate and robust as possible 💰 Cost - Monitoring: prefer cheaper models, otherwise the additional inference cost per generation will add up exponentially - Testing: willing to take on higher costs, calculation is accurate testing leads to better performing outputs 🛑 Risk - Monitoring: if your monitoring judge gets something wrong, there is a high consequence because it directly impacts the end user - Testing: if your testing judge gets something wrong, async review gives some potential to remediate the issue At the moment, it seems using model as a judge for testing is a little more popular due to the high cost constraints of constant monitoring, but that is subject to change as more specialized models come out. As the industry matures, I strongly believe we're gonna see specialization between models for monitoring and models for testing. If you're currently using model as a judge techniques, would love to chat more! Let me know what you think. Twitter Handle: https://lnkd.in/eAsQvtiu

Post 4: How Good Is Your AI? Model Evaluation 🔍📈 Today’s post is all about model evaluation—the phase where we check if our AI model is truly ready for clinical use. Imagine you’ve just baked a cake. Before serving it to your guests, you’d want to make sure it tastes as good as it looks. Similarly, model evaluation is the process of testing an AI system to ensure it performs well and does so fairly. Beyond Basic Accuracy: While traditional metrics like accuracy, sensitivity, and specificity are important, they only tell part of the story. Just as a doctor considers both test results and patient well-being, we need to assess if our AI model works fairly across different patient groups. For instance, a model might boast high overall accuracy but could perform poorly for minority groups or in settings different from where it was developed. This hidden bias can have serious consequences in a clinical environment. Real-World Testing: Evaluation isn’t just a lab exercise—it should mimic real-world conditions. Think of it like a pilot study in a hospital. You test the AI in various scenarios and settings to uncover potential flaws. What if the model is excellent in one hospital but struggles in another with different patient demographics? Comprehensive evaluation helps identify such issues before full-scale deployment. Why It Matters: A robust evaluation process is essential for ensuring that an AI system is both effective and equitable. Without it, we risk deploying a tool that might inadvertently cause harm, much like serving a cake that looks good but has an ingredient that doesn’t sit well with everyone. Establishing a set of comprehensive metrics that capture performance, fairness, and safety can help mitigate these risks. This phase also paves the way for continuous improvement. Just as feedback from patients and clinicians can refine a treatment protocol, ongoing evaluation allows us to retrain and improve the AI system over time, ensuring it adapts to new data and evolving clinical needs. #ModelEvaluation #AIEthics #DigitalHealth #HealthcareAI #PatientSafety #Innovation #MachineLearning #ClinicalAI #DataScience Further Reading: https://lnkd.in/dtJHttXV