Output Quality Assessments

One of the hottest topics in AI is evals (evaluations). Effective Humans + AI assessment of outputs is essential for building scalable self-improving products. Here is the case being laid out for evals in product development. 🔥 Evals are the hidden lever of AI product success. Evaluations—not prompts, not model choice—are what separate mediocre AI products from exceptional ones. Industry leaders like Kevin Weil (OpenAI), Mike Krieger (Anthropic), and Garry Tan (YC) all call evals the defining skill for product managers. 🧭 Evals define what “good” means in AI. Unlike traditional software tests with binary pass/fail outcomes, AI evals must measure subjective qualities like accuracy, tone, coherence, and usefulness. Good evals act like a “driving test,” setting criteria across awareness, decision-making, and safety. ⚙️ Three core approaches dominate evals. PMs rely on three methods: human evals (direct but costly), code-based evals (fast but limited to deterministic checks), and LLM-as-judge evals (scalable but probabilistic). The strongest systems blend them—human judgments set the gold standard, while LLM judges extend coverage and scalability. 📐 Every strong eval has four parts. Effective evals set the role, provide the context, define the goal, and standardize labels/scoring. Without this structure, evals drift into vague “vibe checks.” 🔄 The eval flywheel drives iteration speed. The intention should be to drive a positive feedback loop where evals enable debugging, fine-tuning, and synthetic data generation. This cycle compounds over time, becoming a moat for successful AI startups. 📊 Bottom-up metrics reveal real failure modes. While common criteria include hallucination, safety, tone, and relevance, the most effective teams identify metrics directly from data. Human audits paired with automated checks help surface the real-world patterns generic metrics often miss. 👥 Human oversight keeps AI honest. LLM-as-judge systems make evals scalable, but without periodic human calibration, they drift. The most reliable products maintain a human-in-the-loop review loop—auditing eval results, correcting blind spots, and ensuring that automated judgments remain aligned with real user expectations. 📈 PMs must treat evals like product metrics. Just as PMs track funnels, churn, and retention, AI PMs must monitor eval dashboards for accuracy, safety, trust, contextual awareness, and helpfulness. Declining repeat usage, rising hallucination rates, or style mismatches should be treated as product health warnings. Some say this case is overstated, and point to the lack of reliability of evals or the relatively low current in use in AI dev pipelines. However this is largely a question of working out how to do them well, especially effectively integrating human judgment into the process.

Building Trust in AI: Addressing the Challenge of LLMs Hallucinations As the use of Large Language Models (LLMs) grows, so does a critical challenge: hallucinations, where the model generates unreliable or incorrect outputs. This research paper explores innovative methods to detect and mitigate these hallucinations, offering valuable insights for those deploying LLMs in practical settings. 🔹 Research Focus The paper proposes a framework for assessing LLM output reliability across contexts. It benchmarks state-of-the-art scoring methods for detecting hallucinations and introduces a multi-scoring approach for improved performance. 🔹 Single-generation Scoring This method involves evaluating the reliability of a single generated response. Techniques such as inverse perplexity measure the model's confidence in its output, while the P(True) method prompts the model to verify the correctness of its response. These methods are essential for assessing the quality of outputs when only one response is available. 🔹 Multi-generation Scoring These methods, like SelfCheckGPT, assess the consistency of multiple outputs generated from the same input. By comparing these outputs, the method can identify discrepancies that indicate potential hallucinations. This approach is particularly useful when a model can produce various correct responses, allowing for a more nuanced understanding of the output's reliability. 🔹 Calibration Techniques Calibration ensures that scores accurately indicate the likelihood of hallucinations in outputs. This allows organizations to set thresholds that balance false positives and negatives, leading to more confident decision-making. It addresses the inherent uncertainty in detecting hallucinations, even among human evaluators. 🔹 Cost-effective Multi-Scoring This method optimizes the use of multiple scoring techniques while managing computational costs. By selecting the best-performing scores within a fixed budget, this approach makes the deployment of advanced hallucination detection methods feasible in real-world applications, where resource constraints are often a concern. 📌 Key Insights The findings show that detecting hallucinations in LLMs is complex, with no universal method. The proposed multi-scoring framework, with proper calibration, offers a reliable solution for accurate LLM outputs. This work is crucial for businesses aiming to use LLMs responsibly and reduce misinformation risks, with practical applications in customer service, content creation, and data analysis. 👉 What are your thoughts on the future of LLMs in critical applications, considering these advancements in hallucination detection? How do you plan to implement these strategies in your organization? Share your insights or questions below! 👈 #LLM #LLMs #NLP #NaturalLanguageProcessing #AI #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #FutureOfWork #Automation #TechInnovation #Innovation

🛑 Stop evaluating your AI agents. Start diagnosing them. We're building autonomous AI that can take complex actions on behalf of our businesses. Yet, many are still using last-generation metrics like accuracy to measure them. This is a critical mistake. An agent that gets the right answer through a flawed, risky process is a silent threat. The real risk isn't in the final output; it's in the actions the agent takes to get there. A successful evaluation must analyze the quality of the entire problem-solving path, not just whether it arrived at a correct destination. The Modern Agentic Stack Here’s the stack that makes this diagnostic approach possible: 📝 The Prompt Layer: This is your agent's source code for thought. Instead of messy text files, you use a structured format like POML (Prompt Orchestration Markup Language) to create version-controlled, machine-readable, and auditable instructions. 🔭 The Observability Layer: You can't diagnose what you can't see. This layer uses tools like OpenTelemetry and Graph Databases (e.g., Neo4j) to create a detailed Execution Graph of every single action and thought the agent has. ⚖️ The Evaluation Layer: This is the diagnostic engine itself. A framework like Auto-Eval Judge performs a cognitive autopsy on the execution graph. It doesn't just check the final answer; it assesses the logic of each step, how tools were used, and the efficiency of the reasoning path. 🌱 The Improvement Layer: Why This Matters for RL This diagnostic approach provides a dense, high-quality reward signal that solves two of the biggest problems in RL: It prevents reward hacking: By rewarding a robust and logical process, you stop the agent from learning to cheat the system to get a reward for a poor-quality outcome. It solves sparse rewards: Instead of a single reward at the end of a long task, the agent gets feedback on its intermediate steps, such as the quality of its self-reflection. This makes learning dramatically more efficient and effective. The output is a rich, actionable report detailing the failure. This report automatically could trigger improvement frameworks like SEAL or TPT to generate new training data or fine-tune the agent's logic, creating a closed loop of self-improvement. This is the shift from building static AI to cultivating evolving, intelligent systems.

Can AI Reliably Judge Code Quality? New Benchmark Exposes Critical Challenges 👉 Why This Matters Automated evaluation of code outputs is essential for improving LLMs in software engineering tasks. Traditional metrics rely on human references, but LLM-as-a-Judge paradigms promise scalable assessment without ground truth. However, coding scenarios present unique challenges: subtle errors, varying coding styles, and complex functional requirements make objective evaluation difficult. Existing benchmarks lack the depth and diversity needed to assess modern LLM judges. 👉 What CodeJudgeBench Reveals This new benchmark evaluates 26 models across three critical tasks: - Code Generation (identifying correct solutions) - Code Repair (assessing error fixes) - Test Generation (validating unit tests) Key findings challenge conventional wisdom: - Thinking models dominate: Models using chain-of-thought reasoning (like Claude 4 and Gemini 2.5 Pro) outperform specialized judge-tuned models by up to 25% accuracy - Size ≠ capability: An 8B parameter model (Qwen3-8B) matched performance of 70B parameter alternatives - Position bias persists: Simply swapping response order reduced accuracy by 11% in some models - Test generation proves hardest: Models averaged 69% accuracy here vs 75% in code repair 👉 How Judgment Strategies Impact Results The study compared evaluation approaches: - Pair-wise > Point-wise: Direct comparisons yielded 15-20% higher accuracy than scoring individual responses - Context matters: Keeping original comments and reasoning chains in responses boosted accuracy by 9% - Model bias exists: Judges performed better on Claude-generated code than Gemini outputs, despite equivalent correctness Implications for Practitioners 1. Prioritize reasoning-capable models for code evaluation 2. Use position-swapped testing to identify judgment bias 3. Preserve full response context (code + comments) during evaluation 4. Treat test generation accuracy as a key capability benchmark The CodeJudgeBench dataset is openly available on HuggingFace, enabling teams to stress-test their evaluation pipelines. While current LLM judges show promise, their inconsistency with response ordering and model-specific biases highlight the need for more robust assessment frameworks. This work establishes crucial baselines for developing reliable AI-powered code evaluation systems. The findings suggest that true "objective" LLM judging remains elusive, but provides clear pathways for improvement through better prompting strategies and model architectures.

I've spent 10+ years fixing failed AI deployments in huge companies like Microsoft. Here are 8 systematic checks that serious teams always run: 1. Redundancy Hallucinations are obvious. Repetition is sneakier. LLMs love to circle phrases ("in conclusion," "it's important to note"). Good evals catch these loops - because in ops, wasted words = wasted trust. -- 2. Compression A 1,000-word summary isn't a summary. Strong evals ask: "Can this be cut by 20% without losing meaning?" If the answer is yes, the model isn't doing its job. -- 3. Factual drift The most dangerous failure mode isn't hallucination. It's a summary that sounds accurate but quietly drops or twists a fact. Evaluations run line-by-line cross-checks against the source to prevent silent errors. -- 4. Ordering logic Rankings feel authoritative - but are they? Teams check whether "top recommendations" are actually ordered by a consistent signal, not random chance. -- 5. Tone alignment Ops work is often client-facing. A perfectly accurate draft that sounds robotic or defensive can still tank trust. Evals measure tone against real examples of acceptable communication. -- Consistency 6. One example might look good. Ten might not. Teams run tests across batches to see if tags, categories, or structures hold steady under variation. -- 7. Cost-to-value Eval isn't just about output quality. It's about ROI. If the token bill doubles, does the output double in usefulness? If not, downgrade the model or trim context. -- 8. Latency-to-utility Speed isn't everything. But if an answer takes 18 seconds and users only wait for 6, quality is irrelevant. Latency evals don't measure time; they measure patience thresholds. -- The difference between "it looks fine" and "it works every time" is eval discipline. These checks are how good teams turn LLMs into dependable systems. 🚀 P.S. Want more evaluation frameworks like this? We share systematic testing approaches and reliability playbooks every week in our free AI Product Accelerator community. ↪️ Link in the comments. 100% free to join.

🟢 Yes, AI Evals at scale is what you care about—but how do you make the most strategic decisions? Here’s my advice. The first question in designing an AI Evals strategy is simple but critical: 🎯 Are you evaluating a small-scale PoC or a scaled AI system in production? This decides how much manual effort is acceptable to what level of automation and observability is required. 1️⃣ If you’re building a PoC or Pilot At this stage, Evals won’t block your experimentation, but ignoring them will block future scaling. Ask yourself: ❓ Is the AI output deterministic or rule-based? That is, does the AI produce a predictable, structured format (e.g., JSON) that can be evaluated using fixed logic or known rules? ✅ If yes, use Rule-based Evals to check format, keywords, or logical constraints. ❌ If not, meaning the output is more subjective or nuanced, it needs evaluation by an intelligent agent—either a human expert or an LLM—depending on the availability and maturity of Ground Truth. ✅ If you have Ground Truth, use LLM-as-a-Judge to simulate scoring at scale. ❌ If not, rely on Human Expert Review to assess quality until clear evaluation patterns emerge. This may not scale, but it’s sufficient during early experimentation. Even lightweight evals using open-source tools like DeepEval (https://lnkd.in/gS6PyxQr) can help you define what “good” looks like—and make future automation easier. 2️⃣ If you’re evaluating a scaled AI system in production At production scale, evaluation must scale too. Lightweight approaches won’t keep up, and decisions must be data-driven and continuous. You’ll ask the same question: Is the output deterministic? ✅ If yes, integrate Rule-based Evals into your CI/CD and SRE practices, along with AI Observability to quickly detect drifts, regressions, and failures. ❌ If no, check if there’s Ground Truth with well-defined evaluation criteria: ✅ If yes, use LLM-as-a-Judge to assess subjective outputs (e.g., summarization, ranking) at both the model and RAG module levels. ❌ If no, unlike in early experimentation where human judgment might be good enough, this won’t scale. Begin preparing your Ground Truth for guide LLM to judge —for example, standardized Q&A with validated key facts. Another strategic question: ❓Does the AI application require live data-based evolution or knowledge updates? ✅ If yes, incorporate In-product Telemetry Evals. Monitor model performance through real-time user feedback, success rates, or behavioral data. Analyze this and collaborate with human experts to continuously improve. ❌ If no, go with the classic LLM-as-a-Judge approach is sufficient. In this scenario, you’ll combine rule-based checks, LLM scoring, and telemetry signals to form a feedback loop that enhances both model performance and user trust at scale. #aievals #llm-as-a-judge #aistrategy

How do we evaluate the quality of the LLM evaluator? Human evaluation of LLM output is cubersome and unscalable, so LLMs are increasingly used to evaluate LLM-generated outputs. Yet LLM-generated evaluators simply inherit all the problems of the LLMs they evaluate, requiring further human validation. I am somewhat skeptical about LLM-based evaluation but what is the alternative? If we ask humans to check every single LLM output, we are simply trading one manual task with another, killing the use case. Question is, how do we evaluate the quality of the LLM evaluator? One way to do it to is make sure LLM evaluator aligns with human evaluators. We want to compare human annotations of LLM output against automated (LLM-based) evaluations to measure how well LLM evaluation aligns with human judgment. A high alignment score gives the confidence in accepting the quality of our LLM system. 💡 The goal behind this is not just evaluating the LLM's output, but also evaluating how well the LLM itself can be used as an evaluator (LLM-based evaluation) Eugene Yan created an excellent flowchart to help choose the right evaluation metrics for assessing LLM evaluators. Here’s a breakdown: 1️⃣ Is the task objective or subjective? - Objective (e.g., factuality, toxicity) → Use direct scoring. - Subjective (e.g., persuasiveness, coherence) → Use pairwise comparison (compare multiple LLM responses). 2️⃣ For objective tasks, measaure agreement between LLM Evaluator and human judgments of LLM output: - If evaluation result is binary: use Classification metrics e.g Precision, recall, Cohen’s Kappa. - If not binary, use correlation metrics like Spearman’s Rho, Kendall’s Tau. 3️⃣ For subjective tasks, use pairwise comparison - Compare LLM-generated responses and chooses the better of two responses - Compare human evaluation vs. LLM Evaluator for the same response pairs. - Use Cohen’s Kappa 4️⃣ Are you confident in human evaluations ("ground truth")? - No? Stick with pairwise comparisons . - Yes? Convert to a classification problem. 5️⃣ Do you need it as an evaluator during development, or as a guardrail in production? - If using it as an evaluator during development, you’ll likely evaluate only a few hundred samples and can tolerate the latency/cost of prompting an LLM API. - If using it as a guardrail in production (low latency, high throughput), consider investing in finetuning a classifier or reward model Link to his blog post (it is a long read 😊): https://lnkd.in/eYJx2sxK

If you’ve ever tried to measure the quality of a large language model’s (LLM) responses, you know it’s no small feat. Traditional metrics - like BLEU scores for translation - don’t always capture the nuances of complex, human-like responses. That’s where the concept of using LLMs themselves as judges comes in. A recent paper I looked into provides a really thoughtful structure for thinking about LLM-as-a-judge methods. It breaks the problem down into three clear angles: - What to Judge: Which elements of a response are you evaluating? Accuracy? Creativity? Coherence? - How to Judge: Are you scoring answers directly, comparing them to a reference, or having the model rank multiple responses? - Where to Judge: Is the evaluation happening inline as the response is generated, or is it done after the fact on a separate platform or dataset? They also present several benchmarks that illustrate various methods of having LLMs assess responses. Even if these benchmarks aren’t directly plug-and-play for your specific domain (especially in enterprise scenarios with highly specialized data), they can help you understand patterns and best practices. What I found particularly useful was seeing how LLM-as-a-judge can sometimes outperform standard metrics - if properly calibrated. Calibration is key, because these models can still be biased or drift in their judging standards over time. If you’re just starting out with the idea of LLMs evaluating their own outputs, this taxonomy provides a great roadmap. It can help you figure out what’s feasible, where to start, and which pitfalls to watch out for. Have any of you experimented with letting an LLM serve as its own judge? What approaches or challenges have you encountered? #innovation #technology #future #management #startups

AI Agents in Production, a Golden Standard Evaluation? In traditional software development, regression testing and QA are established disciplines—anchored by deterministic outputs and test cases. But in the world of AI agents, especially those powered by large language models, those same patterns begin to break down. The outputs are probabilistic. The logic is emergent. The behavior changes with updated KB/new data, new logic and/or prompts, or even a model refresh. And yet, most companies are shipping these systems into high-stakes, user-facing workflows every day. This was even more clear when I built a high-metric automation AI Agent stack for Customer Experience & business workflows. Spot-checks (i.e conversational AI transcripts) or crowdworkers are too granular to measure AI agent output quality and base AI system decisions from. So how do we do it? One of the most effective patterns I've seen working is the industry concept of a golden dataset—which is a curated set of inputs, scenarios, and expected outcomes that serve as a living benchmark for an AI agent's functional performance. Done right, golden rules/datasets become far more than a one-time QA step. They become a source of truth even before agent development, and evolving constant tool for alignment, as the AI agent matures. A diagnostic lens into how your agents behave across edge cases, common flows, and failure modes. Effective golden rules: - Reflect both qualitative & quantitative evaluation criteria (i.e. accuracy, completeness, adherence to tone, clarity, relevance, and more) - Evolve alongside your agents, NOT be static. - They blend both automation and human insight. I've seen that the teams that scope early and embed evaluation into the very core of their agent lifecycle will be the ones who can safely move the fastest. Curious how others are approaching this. What does your AI evaluation loop look like in production? PS: If you've noticed I'm grateful to be joining the brilliant minds at Qurrent. We're moving fast, and I’m lucky to be part of it. 🚀

Evaluating outputs from large language models (LLMs) can be challenging. It’s not as simple as asking, “Is this good or not?” 🧠 But sometimes, we need to simplify. To make decisions, we often reduce evaluation to a binary—good or bad, useful or not. That’s where having a structured framework helps. In my work building LLM applications (and teaching others how to do the same), I’ve found evaluation is rarely one-dimensional. Many factors come into play, and they don’t always align. For example: ✔️ An output can be factually correct but incomplete. ❌ Or it can be complete but irrelevant to the task. To navigate this, I use a framework of key dimensions, including: • Correctness: Is the output accurate and factually grounded? • Relevance: Does it address the query or task? • Completeness: Does it include all necessary details? I’ve included a snapshot below that outlines these dimensions. Check out the comment for additional dimensions to consider. What do you think? What dimensions do you prioritize when evaluating LLM outputs?