Evaluating Project Performance Metrics

I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to manually examine and judge outputs longer — than they should. This is because building evals is viewed as a massive investment (say, creating 100 or 1,000 examples, and designing and validating metrics) and there’s never a convenient moment to put in that up-front cost. Instead, I encourage teams to think of building evals as an iterative process. It’s okay to start with a quick-and-dirty implementation (say, 5 examples with unoptimized metrics) and then iterate and improve over time. This allows you to gradually shift the burden of evaluations away from humans and toward automated evals. I wrote previously in The Batch about the importance and difficulty of creating evals. Say you’re building a customer-service chatbot that responds to users in free text. There’s no single right answer, so many teams end up having humans pore over dozens of example outputs with every update to judge if it improved the system. While techniques like LLM-as-judge are helpful, the details of getting this to work well (such as what prompt to use, what context to give the judge, and so on) are finicky to get right. All this contributes to the impression that building evals requires a large up-front investment, and thus on any given day, a team can make more progress by relying on human judges than figuring out how to build automated evals. I encourage you to approach building evals differently. It’s okay to build quick evals that are only partial, incomplete, and noisy measures of the system’s performance, and to iteratively improve them. They can be a complement to, rather than replacement for, manual evaluations. Over time, you can gradually tune the evaluation methodology to close the gap between the evals’ output and human judgments. For example: - It’s okay to start with very few examples in the eval set, say 5, and gradually add to them over time — or subtract them if you find that some examples are too easy or too hard, and not useful for distinguishing between the performance of different versions of your system. - It’s okay to start with evals that measure only a subset of the dimensions of performance you care about, or measure narrow cues that you believe are correlated with, but don’t fully capture, system performance. For example if, at a certain moment in the conversation, your customer-support agent is supposed to (i) call an API to issue a refund and (ii) generate an appropriate message to the user, you might start off measuring only whether or not it calls the API correctly and not worry about the message. Or if, at a certain moment, your chatbot should recommend a specific product, a basic eval could measure whether or not the chatbot mentions that product without worrying about what it says about it. [Truncated due to length limit. Full text: https://lnkd.in/gygj3y7w ]

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

"Why does our top performer get the worst reviews?" the VP asked me. I was reviewing their annual performance data. "Show me," I said. She pulled up the ratings. Diana: 2.8 out of 5. Below average on "collaboration." Low marks for "team player." "What's her actual performance?" I asked. "Exceeded every target. Landed our biggest client. Trained three new hires." "So why the low scores?" "Her peer reviews are dragging her down." I scanned the comments. "Too direct." "Challenges ideas too much." "Not supportive enough." "Let me talk to Diana," I said. "I used to give honest feedback," Diana told me. "Said our pricing model was broken. Got dinged for 'negativity.'" "What happened with the pricing?" "They finally fixed it six months later. After we lost two major accounts." "What else?" "I questioned why we needed  eleven approvals for a simple contract change. Manager said I wasn't being collaborative." "Are you still giving feedback?" "No. I learned my lesson. Now I smile. Nod. Say everything's great. My reviews are improving." "But nothing's actually improving?" "We're making the same mistakes. Just with better vibes." She chuckled. I went back to the VP. "Your review system doesn't measure performance," I said. "It measures compliance." "That's not true." "When was the last time someone got promoted for challenging bad ideas?" Silence. "When did someone get rewarded for preventing a mistake?" More silence. "You've trained your best people to stay quiet. And your mediocre people to stay nice." A few months later, they redesigned the system. Added a category: "Constructive Challenge." Points for identifying problems early. Rewards for preventing costly mistakes. Diana got promoted. "What changed?" I asked the VP. "We stopped confusing agreement with alignment. Stopped mistaking silence for harmony." "And?" "Turns out our 'difficult' people were our most valuable. They actually cared enough to speak up." Here's the truth about performance reviews: Most companies don't reward performance. They reward performance theater. The person who says the meeting was great beats the person who says it wasted an hour. The person who agrees with bad ideas beats the person who prevents disasters. You think you're measuring contribution. You're measuring conformity. And your best people? They've already figured out the game. They're just deciding whether to play it or find somewhere that values truth over comfort. _____ Like my content? Give me a follow. Want to see more of it? Click the 🔔 on my profile.

If you benchmark projects on €/kWp, you miss the point. The real metric is €/MWh. In practice, I keep running into the same discussions: How do you compare Project A (say, in Eastern Europe) with Project B (say, in Southern Europe), when grid, construction, O&M or financing have totally different cost profiles? Instead of arguing over individual cost items, there’s a simpler way: look at LCOE (€/MWh). What really matters (short & clear): --> €/kWp = construction indicator, but not a success factor. --> LCOE (€/MWh) captures CAPEX, OPEX, performance (PR/degradation), financing & lifetime. --> A “more expensive” project can deliver cheaper power thanks to higher yield, longer lifetime, or better financing. --> Investors and banks already benchmark on €/MWh, not €/kWp. Number flavor (utility scale, all-in incl. EPC, development, financing): -->Typical Utility Scale DE/CEE (2024): ~560–600 €/kWp all-in -->Project A: 580 €/kWp, PR 80%, WACC 6%, 25 years -> ~49-52 €/MWh -->Project B: 640 €/kWp, PR 87%, WACC 5%, 30 years -> ~40-43 €/MWh --> Same installed capacity, different assumptions –> output beats input. Do you still benchmark projects on €/kWp? Or already on €/MWh? And which 3 variables move your LCOE the most: PR, WACC, O&M, degradation? #AndreasBach #LCOE #SolarPV #ProjectFinance #CleanEnergy

I have put together this DevOps Metrics infographic - it's like a cheat sheet for keeping your finger on the pulse of your entire development pipeline. Let's break it down- We start with the "Plan" phase - because hey, failing to plan is planning to fail, right? 😉 We're talking Sprint Burndown, Team Velocity, and even Epic Burndown. These metrics help you understand if your team is biting off more than they can chew or if they're ready to take on more challenges. Moving on to "Code" - this is where the rubber meets the road. Code Reviews, Code Churn, Technical Debt - these aren't just buzzwords, folks. They're vital signs of your codebase's health. And don't get me started on the importance of Maintainability Index! The "Build" and "Test" phases are where things get real. Build Success Rate, Test Coverage, Defect Metrics - these are your early warning systems. They'll tell you if you're building on solid ground or if you're in for a world of hurt down the line. Now, "Release" and "Deploy" - this is where many teams start sweating. But with metrics like Release Duration, Deployment Frequency, and Change Failure Rate, you can turn this nail-biting phase into a smooth, predictable process. Finally, "Operate" and "Monitor" - because your job isn't done when the code hits production. Customer Feedback, System Uptime, Mean Time to Detect and Repair - these metrics ensure you're not just shipping code, but delivering value. The best part? I've included some of the go-to tools for each phase. Jira, GitHub, Gradle, Jenkins, Docker, Kubernetes - these aren't just fancy names, they're the workhorses that'll help you track these metrics without losing your mind. Remember, folks - you can't improve what you don't measure.

"The LLM works great." Works great… according to what? That's the question most AI teams skip, and it's why so many models look brilliant in demos and fall apart in production. Testing an LLM isn't one thing. It's six, and using only one of them is how trust quietly breaks. Here are the 6 methods for testing LLM output quality 👇 🔹Human Evaluation - the gold standard for nuance, tone, subtle errors. Slow and costly, but irreplaceable. 🔹Automated Metrics - BLEU, ROUGE, BERTScore, perplexity. Fast and repeatable, weak on meaning. 🔹Adversarial & Red-Teaming - stress tests for jailbreaks, prompt injection, hallucinations. Critical before launch. 🔹LLM-as-a-Judge - a strong model grades outputs. Scales human-like judgment cheaply (watch for bias). 🔹Task-Specific Evaluation - custom datasets that mirror production. Measures real business value. 🔹Benchmark Testing - MMLU, HellaSwag, GSM8K, HumanEval. Comparable across models; may miss real-world tasks. The takeaway: no single method covers everything. Layer them. Save this if you build with LLMs. Which do you trust most? 👇

🔍 Diving into LLM System Metrics: What Really Matters After analyzing six months of LLM deployment data, here are the metrics that actually matter: ⚡ Reliability: 99.99% uptime - because enterprise solutions demand consistency ⏱️ Response Time: 500ms average - crucial for real-time applications 📈 Scale: Processing 10B+ tokens weekly across enterprise workloads 🔒 Security: 256-bit encryption, with <0.001% unauthorized access attempts 💰 Efficiency: Adaptive token allocation reducing operational costs by 30% 🧠 Intelligence: 5 specialized models, each learning from 1M+ daily interactions What stands out is how these metrics are evolving. While response time was the focus couple of years back, we're seeing a clear shift toward efficiency and specialized performance metrics in 2025. 💭 Curious to hear from other AI practitioners: Which metrics are you prioritizing for your LLM systems this year?

Your regenerative pilot cost $200k. It died after 18 months. Here's why it failed (and how the next one won't). I've done post-mortems on multiple failed regenerative programs in the last decade. The pattern is always the same. Companies launch pilots without answering three questions first: 1. What business issue are we solving? Not "what looks good in the sustainability report." Most programs start with: "We need to launch something regenerative." They should start with: "We have yield volatility in Region X" or "We're losing suppliers in Region Y" or "Raw material quality is declining in Z." Regenerative agriculture is not a goal. It's a lever when tied to a real business problem. 2. Who owns transition risk in years 1-2? Spoiler: it's not the farmer. In the first years of transition: • Yields can fluctuate • Management becomes more complex • Results depend on soil history, climate, farmer skill If the company doesn't absorb this risk, farmers exit after one bad season. I've seen this kill dozens of programs (and the next year a "new one" starts with the same issue). 3. What happens when yields drop >15%? Because they might, temporarily. Most pilots are designed assuming stable or improving yields from day one. Reality: regenerative transitions have a J-curve. Performance dips before it improves. If the program design doesn't account for this, procurement panics, the pilot gets labeled "failure," and no one wants to try again. Here's what programs that scale do differently: They define success before defining practices. They design farmer support systems, not just practice checklists. They absorb transition volatility instead of pushing it downstream. They measure system resilience, not just carbon sequestered. The difference between programs that die in year 2 and programs that scale in year 5 is visible in the design phase. Most teams just don't know what to look for. If you're designing a regenerative program right now, there's a diagnostic that pressure-tests whether it's built to scale or built to fail. https://lnkd.in/ekx6-rGy Let's make sure the next one survives.

Evaluating LLMs is not like testing traditional software. Traditional systems are deterministic → pass/fail. LLMs are probabilistic → same input, different outputs, shifting behaviors over time. That makes model selection and monitoring one of the hardest engineering problems today. This is where Eval Protocol (EP) developed by Fireworks AI is so powerful. It’s an open-source framework for building an internal model leaderboard, where you can define, run, and track evals that actually reflect your business needs. → Simulated Users – generate synthetic but realistic user interactions to stress-test models under lifelike conditions. → evaluation_test – pytest-compatible evals (pointwise, groupwise, all) so you can treat model behavior like unit tests in CI/CD. → MCP Extensions – evaluate agents that use tools, multi-step reasoning, or multi-turn dialogue via Model Context Protocol. → UI Review – a dashboard to visualize eval results, compare across models, and catch regressions before they ship. Instead of relying on generic benchmarks, EP lets you encode your own success criteria and continuously measure models against them. If you’re serious about scaling LLMs in production, this is worth a look: evalprotocol.io

GCC Leaders: Are You Measuring What Truly Matters? To measure the real impact of your Global Capability Center (GCC), you must go beyond traditional operational KPIs like cost savings or headcount. Those are hygiene. What truly matters is how your GCC moves the needle for the business. Here are 5 strategic metrics every GCC leader should track: 1. Value Delivered per Dollar Spent Why it matters: Shows how effectively the GCC converts investment into business outcomes. How to measure: • Business value (e.g., product revenue, productivity gains, IP created) / Total GCC cost • Can be benchmarked against alternative models (outsourcing, onshore) 2. Time to Market Acceleration Why it matters: Reflects the GCC’s ability to improve speed of execution for product development, support, or operations. How to measure: • % improvement in release velocity or cycle times after GCC involvement • Lead time from idea to launch before vs. after GCC enablement 3. Innovation Output Why it matters: Indicates contribution toward competitive advantage and future growth. How to measure: • Patents filed, features launched, automation use cases deployed • Number of AI/GenAI initiatives incubated and scaled • New product ideas or MVPs driven from GCC 4. Business Function Ownership & Accountability Why it matters: Measures the maturity and strategic importance of the GCC. How to measure: • % of global business function fully owned or co-owned by GCC (e.g., platforms, support functions, analytics COEs) • Strategic roles (Directors, VPs) based in the GCC • Participation in global decision-making forums 5. Customer or Stakeholder NPS / Satisfaction Score Why it matters: This metric reflects how well the GCC is delivering value—both through the products it helps build and the support it provides to global stakeholders. How to measure: • NPS from external customers using products or services developed by GCC teams • NPS from internal stakeholders on the GCC’s responsiveness, collaboration, and strategic alignment • Qualitative feedback on product quality, innovation, speed of execution, and business understanding If your GCC isn’t driving the business forward, it’s just another offshore team. And in 2025, that’s not enough. Rethink how you measure. Reframe how you lead. Redefine what your GCC stands for. Zinnov Amita Goyal Karthik Padmanabhan Amaresh N. Mohammed Faraz Khan Namita Adavi Dipanwita Ghosh Sagar Kulkarni Hani Mukhey ieswariya Rohit Nair Komal Shah Saurabh Mehta