Tech-Driven Performance Reviews

LLM applications are frustratingly difficult to test due to their probabilistic nature. However, testing is crucial for customer-facing applications to ensure the reliability of generated answers. So, how does one effectively test an LLM app? Enter Confident AI's DeepEval: a comprehensive open-source LLM evaluation framework with excellent developer experience. Key features of DeepEval: - Ease of use: Very similar to writing unit tests with pytest. - Comprehensive suite of metrics: 14+ research-backed metrics for relevancy, hallucination, etc., including label-less standard metrics, which can quantify your bot's performance even without labeled ground truth! All you need is input and output from the bot. See the list of metrics and required data in the image below! - Custom Metrics: Tailor your evaluation process by defining your custom metrics as your business requires. - Synthetic data generator: Create an evaluation dataset synthetically to bootstrap your tests My recommendations for LLM evaluation: - Use OpenAI GPT4 as the metric model as much as possible. - Test Dataset Generation: Use the DeepEval Synthesizer to generate a comprehensive set of realistic questions! Bulk Evaluation: If you are running multiple metrics on multiple questions, generate the responses once, store them in a pandas data frame, and calculate all the metrics in bulk with parallelization. - Quantify hallucination: I love the faithfulness metric, which indicates how much of the generated output is factually consistent with the context provided by the retriever in RAG! CI/CD: Run these tests automatically in your CI/CD pipeline to ensure every code change and prompt change doesn't break anything. - Guardrails: Some high-speed tests can be run on every API call in a post-processor before responding to the user. Leave the slower tests for CI/CD. 🌟 DeepEval GitHub: https://lnkd.in/g9VzqPqZ 🔗 DeepEval Bulk evaluation: https://lnkd.in/g8DQ9JAh Let me know in the comments if you have other ways to test LLM output systematically! Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

By the time a top performer hands in their resignation, the battle was already lost months ago… If your HR dashboard only tells you who resigned last month, you are driving your company while looking in the rearview mirror. Let's talk about the difference between “forensic” metrics and cultural prediction. Most executive tables still look at monthly turnover rates and exit interviews to measure organizational health. But as I mentioned, when that resignation letter arrives, it is just the final symptom. Exit interviews don't fix the culture; they just document the casualties. Managing complex operations across LatAm has taught me that in high pressure environments, reactive HR is a luxury you simply cannot afford. The real power of AI in People Analytics is not generating prettier charts, it is moving HR from post mortem reporting to predictive design. By crossing and analyzing variables like meeting overload, lack of focus time, and collaboration metadata, we can spot the early signs of collective burnout before the damage is done. (of course, among others!) To make this shift, we need to change how we use our data: • Stop the post mortem: Shift your energy from exit interviews to predictive "stay" analytics. • Connect the invisible dots: Use AI to correlate digital workload data with cultural erosion. • Deploy targeted empathy: AI does not replace the human touch; it tells leadership exactly which team needs an intervention today. Technology should help you protect your culture, not just count who left because of it. Is your HR team predicting your next talent crisis, or just reporting on the last one? #PeopleAnalytics #HRLeadership #FutureOfWork #AIStrategy #LatAmBusiness #CHRO

One thing I keep seeing in enterprise AI deployments: the models that look best on benchmarks, struggle in production. It's not that benchmarks are wrong. They're just measuring different things than what matters when a customer is on the phone, or when an agent needs to orchestrate a 9-step workflow across multiple systems. It's not just the model that matters but the platform it's running on. A right orchestration can bring advantages to make these agents work. We published two research efforts recently trying to close this gap. If you are building agents in Enterprise, I strongly recommend to look into them. EVA (Evaluation of Voice Agents) measures both accuracy AND experience in spoken conversations. There's a consistent tradeoff and balance between the two: agents good at completing tasks and optimizing conversational experiences. That's not something you'd catch with task-completion metrics alone. 🌐 Check out: https://lnkd.in/gEB4gkun EnterpriseOps-Gym evaluates agents on 1,150 tasks across real enterprise domains including ITSM, HR, CSM, Calendar. Multi-step workflows, stateful planning, actual tool use. There is plenty of room to improve especially on long horizon planning. 🌐 Check out: https://lnkd.in/gw8Sr2n4 Both are open-sourced. Evals shape what we optimize for. If we want AI that works in enterprise settings, we need evals that reflect enterprise reality. Keep sharing the feedback. An amazing team effort:  Tara Bogavelli, Gabrielle Gauthier Melançon, Katrina Stankiewicz, Oluwanifemi Bamgbose, Hoang Nguyen, Raghav Mehndiratta, Hari Subramani Lindsay Brin, Akshay Kalkunte Suresh, Joseph Marinier, Jishnu S Nair, Aman Tiwari, Fanny Riols, Sridhar Krishna Nemala, Anil Kumar Madamala, Srinivas Sunkara, Shiva Krishna Reddy Malay, @Shravan Nayak, Aman Tiwari, Sathwik Tejaswi Madhusudan, Sagar Davasam, Sai Rajeswar, Patrice Bechard, Vikas Yadav, PhD, Rachel Hansen, Tiffany D., Nidhi Kumari, Lingzhu Li, Raahul Srinivasan, Ravi Krishnamurthy And our partners at Turing (for EnterpriseOps-Gym): Ankit Jasuja, Aakash Chavan, Harshil Parekh, Anuj Jain, Igor Vidal, Rahul Bora, Sudarshan Sivaraman, Saurabh Choudhary Stay tuned for next iteration! #ExperienceFromTheField #WrittenByHuman

🤖 Revolutionizing AI Evaluation: Agents Judging Agents? Evaluating the performance of advanced AI systems has always been a challenge, especially as these systems grow more complex and autonomous. A recent paper, "Agent-as-a-Judge: Evaluate Agents with Agents" by researchers from AI at Meta and KAUST (King Abdullah University of Science and Technology) introduces a groundbreaking framework where agentic systems evaluate other agentic systems. This approach provides rich intermediate feedback, enabling more nuanced and scalable evaluations compared to traditional methods like human judges or LLM-as-a-Judge. 🔆 Key highlights:- 👩💼 The Agent-as-a-Judge framework mimics human evaluation by considering the entire decision-making and action trajectory of AI agents, not just final outcomes. ✨ The framework introduces DevAI, a benchmark with 55 realistic AI development tasks, complete with 365 hierarchical user requirements for rigorous testing. 👉 Results show that Agent-as-a-Judge aligns closer to human consensus (90%) than LLM-as-a-Judge (70%). 🥇 Additionally, Agent-as-a-Judge offers two key advantages:- 1️⃣ Automated Evaluation: Agent-as-a-Judge can evaluate tasks during or after execution, saving 97.72% of the time and 97.64% of costs compared to human experts. 2️⃣ Provide Reward Signals: It provides continuous, step-by-step feedback that can be used as reward signals for further agentic training and improvement. 🌟 This concept could transform how we assess dynamic, multi-step AI systems, unlocking new possibilities for self-improvement and real-world applications. ♾️ If you're passionate about AI innovation and ethical evaluation, I highly recommend diving into this magnificent work:- 📜 Paper - https://lnkd.in/dDN8se_5 🤗 Dataset - https://lnkd.in/d2eYKEJH 📂 Project - https://lnkd.in/dz3BGr3k What are your thoughts on using AI to evaluate AI? Is this the beginning of a self-regulating AI era? #AI #ArtificialIntelligence #Evaluation #AgenticSystems #AIInnovation #aisafety #aialignment

📊How accurately can we predict turnover and workers’ comp claims a year in advance? Turnover and workers' comp claims are costly for organisations and difficult experiences for employees. Knowing where risk is likely to emerge gives HR and Health & Safety teams a chance to proactively manage it. But how accurately can these outcomes be predicted in advance? To explore this, we trained a gradient-boosted decision tree model on data from the Household, Income, and Labour Dynamics in Australia survey (2001–2023), which included 191,000 observations from nearly 25,000 workers. We used predictors that mirror what most HR systems or engagement surveys capture including demographics, tenure, role characteristics, compensation, benefits, and job satisfaction. We trained on 80% of the workers and tested on the remaining 20%. What we found: 🎯 Triple the Accuracy for the Highest-Risk Individuals: The top 3% flagged were 3.5× more likely to actually leave or claim than a random 3%. 🔬Double the Overall Prediction Quality: Across the whole workforce, the model was over twice as good as chance at separating higher- from lower-risk employees. 🔍 Concentrated Risk for Intervention: The top 10% flagged accounted for nearly 3× more cases than expected by chance. What this means: Even a year in advance, a data-driven approach can provide a strong signal to help focus retention and safety efforts. The accuracy, while not perfect, is high enough to be useful, especially when a model like this is used to support the expertise of managers, organisational psychologists, and other specialists. It can help HR and Health & Safety teams develop proactive and targeted risk management efforts. The exciting thing is that this was all with broad, national survey data. With higher-quality internal data from a single organisation, predictive accuracy could be even stronger. But the challenge is making sure the right data is being collected and shared between units and systems, which is often the hardest part of turning analytics into action. #PeopleAnalytics #PredictiveAnalytics #EmployeeTurnover #HRTech #MachineLearning #WorkplaceSafety #DataScience #HR

🔥 Anthropic recently released Bloom for scaling behavioral AI evaluations via agentic frameworks! LLM evaluation is getting harder and harder due to training data contamination. Bloom is a new agentic framework designed to automate the generation and execution of targeted behavioral evaluations for frontier AI models. It transforms researcher-specified behaviors into reliable evaluation suites. TLDR 🤖 Automates evaluation with a 4 stage pipeline from behavioral "Understanding" and scenario "Ideation" to parallel "Rollouts" and automated "Judgment" . 🛡️ Unlike fixed sets, Bloom generates different scenarios on each run, mitigating the risk of evaluation "contamination" in training sets. ⚖️ Alignment eval results show a 0.86 Spearman correlation with human judgments when using Claude Opus 4.1 as a judge. 📉 Successfully separated baseline models from intentionally misaligned "model organisms" in 9 out of 10 test cases. Bloom is open-source, integrated with Weights & Biases for tracking experiments and it supports Inspect-compatible transcripts with a custom transcript viewer. Full technical report with code and example in the comments 👇 #AISafety #OpenSource #ArtificialIntelligence #ModelAlignment #Anthropic

Do you know what machine failure and absenteeism have in common? Both show warning signs before breaking down. For decades, we've been better at predicting when machines will break than when employees will fall sick or leave the company. It's not like employers don’t care. Most organizations know that employee well-being matters. But, in many cases a systemic approach is not accompanied by a sense of urgency. And organizations often wait for some sort of breakdown to happen before they take action. At Kyan Health, we believe the current “right time” is often too late. So, we aim to change the narrative with a new approach: We are building the world's first #predictivecaremodel for organizational health (think of it like an analytical engine that connects dots you never knew existed). Our model takes anonymous data across functions, departments, and countries: • Department-level well-being data • HRIS data (hiring, firing, promotions) • Business news impact • Geopolitical events • Insurance claims • Regional context This creates a real-time risk management system that predicts: • Risk of absenteeism • Presenteeism impact • Positive productivity gains • Potential productivity drops Most importantly, it can forecast the risk three to six months ahead. This means seeing what happens if you take action versus doing nothing. The model is simple, but not easy: Just as machines have sensors, it aggregates data points across various relevant systems. Just as production lines self-correct, it identifies where intervention is needed at organizational and individual levels (all individual interventions are delivered only to the individual, confidentially and on a voluntary basis). And just like assembly lines prevent failures, it minimizes lost time due to mental health challenges. The interplay between individual team members, managers, senior leaders, organizational structures, and policies are A LOT more complex than production lines.  But the principle remains the same: Don't wait for parts of your organization to fail. Predict, intervene, and prevent. The future of workplace mental health is no longer about offering crisis hotlines and meditation apps. It's about predicting and eliminating system failures before they materially impact individual and business performance. ------------------------------------- This is just a glimpse of what we're building at Kyan Health. If you're as excited about predictive care as I am and want your organization to be ahead of the curve and test the model, drop me a DM. We're still fine-tuning things, but I would love to share more about how we could work together when we launch.

Here's the LLM evaluation stack I recommend to every team: Layer 1: Unit Tests (DeepEval) Stop treating AI as a mystery box. Integrate with Pytest to run assertions on every build. → Test individual components (retrievers, generators, tools) → Run in CI/CD to block regressions → Move from vibe-checking to deterministic engineering Layer 2: Metric Suite (50+ SOTA Metrics) Quantify performance with academic-grade metrics, not just "looks good" scores: → Hallucination: Is it making things up? → Faithfulness: Is it strictly grounded in your context? → Agentic Trajectory: Did it pick the right tool and use the correct arguments? → G-Eval: Define custom, subjective criteria in plain English. Layer 3: Synthetic Data Evolution Don't wait for user logs to find your bugs. → Generate thousands of "Golden" test cases from your docs in minutes → Automatically cover complex edge cases → Scale your testing without a single manual label Layer 4: Continuous Monitoring Evaluation doesn't stop at deployment. → Track performance drift in real-time → Get a "Rationale" (the why) for every production failure → A/B test prompt versions with statistical confidence DeepEval handles all 4 layers in one framework. One framework: ✓ 50+ research-backed metrics ✓ Pytest-native syntax ✓ Synthetic data generation ✓ Full Agent & RAG support This is how you ship AI with actual confidence. (100% Open-Source) GitHub Repo - https://lnkd.in/gQ3zCcZN Don't forget to ⭐️

 🤖 The AI agent evaluation problem nobody talks about: You've built an agent. It seems to work. But how do you actually know? Most teams spend weeks building scenario-specific test datasets—time that could be spent on innovation. And even when tests exist, results are often opaque. You can't isolate why an agent passed or failed. That's why we built Evals for Agent Interop—an open-source evaluation kit that brings structure and transparency to agent testing.  What makes it different:  ✅ Curated scenarios with synthetic data (start testing immediately)  ✅ Granular metrics: tool use accuracy, latency, groundedness  ✅ Configurable rubrics for tone, compliance, and accuracy  ✅ Leaderboard to compare agents across frameworks and LLMs  ✅ Built for Microsoft 365 interoperability (Email, Calendar, Teams, Documents) The goal? Move from guesswork to data-driven decisions. From months-long iteration cycles to days or hours.  🔗 Check-out the awesome blog & video from Aadharsh, Darshini, & Alastair: https://lnkd.in/gyaZRB9A