CleverX

We've noticed a pattern in survey research: teams either let unqualified respondents through (contaminating their data), or screen so aggressively that qualified people drop out before reaching the actual questions. The tricky part is that screening questions need to be thorough enough to find the right people, but not so exhausting that they drive everyone away. Some things that help: starting broad and narrowing down, placing sensitive questions after initial qualification, and watching out for "professional survey takers" who learn to game the system. We recently put together a template with pre-tested screening questions organized by criteria type, along with fraud detection measures and quota management approaches Check it out here: https://lnkd.in/dKtJz99B

Research is industrializing and most teams aren't ready for it. Teams want answers faster. Vendors are delivering tools to make that possible. But without clear rules, speed just produces faster mistakes. In the latest issue of The Research Mag, our founder Sharekh Shaikh breaks down what's actually changing: → Expert networks are now a $4.19B market-domain context is buyable at scale → AI tools and ReOps platforms are making depth compound while cycles shorten → The real choice teams face: standardize or shortcut (most think they're doing both well, they're not) It includes: ✓ How to use expert calls without replacing user research ✓ Decision-led method pairings that remove paralysis   ✓ Why "no link = no claim" should be your new standard ✓ How to place economics next to evidence (with examples) The research function that survives the next 3 years won't be the one that ran the most studies. It'll be the one that changed the most decisions and showed revenue impact. Read the full issue 👇 #ProductResearch #ResearchOps #MarketResearch

Are research teams really forced to pick speed or depth? In this month’s The Research Mag, Sharekh Shaikh founder of CleverX, explains how teams are moving faster without thinning the understanding that drives decisions. He covers what actually changed in workflows, where automation helps and where judgment still matters, and why every finding needs a simple business line next to it. Read the issue 👇 #TheResearchMag #MarketResearch #UserResearch #ResearchOps

The AI industry is split on a fundamental question: Should we scale with synthetic data or double down on human feedback? Both approaches are exploding in popularity, but for opposite reasons. Synthetic data promises speed and cost efficiency. Human feedback delivers alignment and breakthrough capabilities. Understanding when to use each isn't just technical - it's strategic. The synthetic data case is compelling: → Generate millions of training examples instantly → Bootstrap competitive baselines quickly → Scale annotation without human bottlenecks → Reduce costs dramatically for routine tasks But synthetic data has a ceiling - it cannot exceed the capabilities of the model that generated it. Human feedback remains irreplaceable for: → Pushing beyond current frontiers: Only humans can teach models capabilities that don't exist yet → Nuanced alignment: Tone, empathy, and cultural sensitivity require human judgment → Safety evaluation: Detecting subtle bias and harmful outputs needs real human reviewers → Novel problem solving: Breaking through performance plateaus requires human creativity The emerging pattern among leading AI teams: Hybrid workflows that leverage both approaches strategically. Synthetic data for: Initial training, scaling volume, catching up to benchmarks Human feedback for: Alignment, safety, pushing capabilities, high-stakes decisions Real-world application strategies: → Use synthetic data to train base models, then fine-tune with human feedback → Let AI handle routine annotation, reserve humans for edge cases and quality control → Generate synthetic examples for data augmentation, validate with human oversight → Scale customer support with synthetic training, refine responses with human evaluation The key question isn't "synthetic or human?" - it's "what combination drives the outcomes we need?" Teams that master this balance build AI that's both scalable and trustworthy. Those that rely too heavily on either approach hit walls - either quality ceilings or cost explosions. How is your team balancing efficiency with the human expertise that creates breakthrough AI? #ArtificialIntelligence #MachineLearning #SyntheticData #HumanFeedback #RLHF

Behind every breakthrough AI system is an army of human annotators you'll never hear about - and they're the reason your AI actually works. Data annotation is the invisible foundation of machine learning. While headlines focus on model architectures and compute power, the quality of training data determines whether AI succeeds or fails in the real world. Without annotation, machines are blind. With poor annotation, they learn the wrong lessons entirely. The annotation challenge is bigger than most teams realize: → Scale: Modern models need millions of labeled examples → Consistency: Different annotators can interpret the same data differently → Bias: Human prejudices embed directly into model behavior → Cost: Quality annotation requires significant time and resources Four annotation approaches that drive AI performance: → Manual annotation: Humans carefully label every example. Slow but highest accuracy for critical applications. → Semi-automated annotation: AI suggests labels, humans validate and correct. Balances speed with quality control. → Active learning: Models identify their most uncertain predictions for human review. Maximizes annotator impact. → Human-in-the-loop workflows: Continuous feedback cycles between humans and models. Essential for alignment and safety. Industries where annotation quality is make-or-break: → Autonomous vehicles need perfect object recognition to avoid accidents → Medical AI requires precise image labeling for accurate diagnosis → Content moderation systems must understand cultural context and nuance → Financial fraud detection depends on accurately labeled transaction patterns The hidden truth about AI success: Your model is only as good as your annotation process. Garbage in, garbage out - but quality annotation in, breakthrough AI out. Organizations winning at AI don't just invest in bigger models. They invest in annotation infrastructure, quality controls, and the human expertise that makes machine learning possible. What's your biggest challenge when it comes to training data quality? #ArtificialIntelligence #MachineLearning #DataAnnotation #DataScience #AITraining

Most AI teams deploy models without knowing if they actually work in production-and the consequences can be catastrophic. Building a model is only half the battle. The real challenge is proving it performs reliably, fairly, and safely in the real world. That's where model evaluation becomes your lifeline. Model evaluation isn't just about accuracy scores. It's about asking the hard questions before deployment: → Does this model work for all user groups, or just the majority? → Can it handle edge cases it's never seen before? → Will it maintain performance as data shifts over time? → Does it align with human values and expectations? Four evaluation approaches every AI team should master: → Cross-validation: Test how well your model generalizes using k-fold techniques. Prevents overfitting disasters. → Human evaluation: Use rubrics to score outputs on relevance, safety, and alignment. Critical for subjective tasks like content moderation. → Continuous monitoring: Track key metrics post-deployment. Catch performance degradation before users do. → Multi-dimensional assessment: Combine quantitative metrics with qualitative judgment. Numbers tell you what happened, humans tell you why it matters. When rigorous evaluation becomes non-negotiable: → Healthcare AI making diagnostic decisions → Financial models flagging fraud or approving loans → Autonomous systems where errors have real-world consequences → Customer-facing AI representing your brand voice The cost of inadequate evaluation? A medical AI that works for 80% of patients but fails for underrepresented groups. A fraud detector that stops working when attack patterns evolve. A chatbot that damages brand trust with biased responses. Strong evaluation isn't just a technical checkpoint - it's what separates AI that works in the lab from AI that works in the world. How does your team balance speed-to-market with thorough model evaluation? #ArtificialIntelligence #MachineLearning #DataScience #AIEvaluation

How do you turn a raw language model into an AI assistant that feels genuinely helpful? It takes more than training, it takes a system. That system is called Reinforcement Learning from Human Feedback (RLHF), and it’s how models evolve from capable to aligned. Here’s how that transformation happens, step by step: Phase 1: Supervised fine-tuning Human experts write examples of ideal behavior across diverse prompts. The model learns to follow instructions-not just predict text. Phase 2: Reward modeling Humans compare model responses. Those preferences are used to train a reward model that reflects what people actually want. Phase 3: Policy optimization (PPO) The model learns to choose responses that maximize reward (i.e., human approval), while preserving its core capabilities. This is where reinforcement learning meets careful constraints. Phase 4: Iterative improvement Post-deployment, feedback loops continue. Edge cases, misalignments, and new expectations are monitored, and used to retrain. Where this process is mission-critical: • Customer-facing AI where trust builds adoption • Healthcare, finance, or law, where context matters as much as accuracy • Any domain where you need consistently helpful, not just technically correct But RLHF isn’t plug-and-play. It demands: • Skilled human labelers • Large-scale feedback infrastructure • Ongoing QA and workflow design • Serious compute Emerging challenges include: • Scaling feedback without sacrificing quality • Managing annotator bias • Avoiding reward hacking • Capturing complex human values in simple comparisons As AI moves from research labs to real-world products, RLHF is what ensures those systems serve people-not just optimize math. 👉 How are you approaching human feedback and model alignment? #ArtificialIntelligence #RLHF #AIAlignment #AITraining #MachineLearning #ResponsibleAI #TechLeadership

Think labeled data is obsolete in the age of large language models? Think again. Even with today’s powerful foundation models and unsupervised learning techniques, labeled data remains essential, especially when you're moving from experimentation to production. Why labeling still matters: → Accuracy: Even the best models need ground truth to learn domain-specific patterns → Safety: Labels help catch harmful or inappropriate outputs before they reach users → Alignment: Labeled preferences power RLHF systems that make AI genuinely helpful Labeling has evolved: It’s no longer just about tagging images or classifying text. Today, it includes: → Ranking responses for RLHF → Evaluating tone, toxicity, and appropriateness → Building prompt-output pairs for fine-tuning → Teaching models to reason within industry-specific domains Where it becomes non-negotiable: → Medical AI distinguishing between similar symptoms → Financial models navigating regulatory compliance → Customer service bots using the right tone in sensitive situations In short: anywhere “close enough” isn’t good enough. What high-performing teams do: → Blend automated labeling with expert human review → Build QA into every stage of the labeling process → Treat labeling as strategic infrastructure—not a side task Cutting corners leads to: → Biased outputs. → Poor generalization. → Avoidable safety risks. → And long-term data debt that slows everything else down. As AI becomes central to business operations, labeling quality often decides whether your models succeed or fail in the real world. 👉 What’s your team doing to ensure labeling quality at scale? #ArtificialIntelligence #DataLabeling #AIQuality #MachineLearning #ResponsibleAI #TechLeadership #DataOperations

Ever wondered why some AI feels helpful while other AI feels robotic, even when both give technically correct answers? Traditional AI training relies on data and mathematical reward functions. But the most effective modern AI systems learn directly from human input and preferences. Human feedback provides three critical capabilities that pure data can't deliver: Alignment - Models learn what "helpful" actually means in real-world contexts, beyond statistical predictions. Safety - Human reviewers catch problematic outputs that automated systems miss. Nuance - Cultural context, tone, and appropriateness that can't be captured in datasets alone. How it works: AI creates multiple responses → humans rank them by quality → models learn these preferences → the cycle repeats, getting better each time. Where this becomes critical: If you're building customer service AI that needs to sound empathetic, not just accurate. If you're in healthcare or finance where context and tone can impact outcomes. If you want AI that reflects your company's values, not just generic "helpful" responses. The implementation challenge: Good human feedback requires real expertise. You need people who understand your domain, consistent ways to evaluate quality, and systems that can learn from disagreement between reviewers. The difference between AI that people trust and AI that people avoid often comes down to this human element in training. How are you approaching AI alignment and evaluation in your organization? #HumanFeedback #RLHF #ArtificialIntelligence #MachineLearning

Over 80% of AI teams now use prompt engineering as their starting point - and the most successful ones layer additional customization techniques on top Many organizations start with prompt engineering to customize their AI models. It's an excellent first step that delivers quick wins for basic customization. As teams scale and need deeper domain expertise, they often add fine-tuning techniques to build on their prompt engineering foundation. Think of it this way: prompt engineering optimizes what you ask the AI. Fine-tuning teaches the AI to think differently about your domain. Used together, they create powerful, specialized AI systems. Four fine-tuning approaches that complement prompt engineering: → Supervised fine-tuning: Uses labeled datasets to train models on specific input-output examples. Ideal when you have clean, domain-specific data. → Parameter-efficient fine-tuning (PEFT): Focuses training on a small subset of parameters, reducing computational costs while maintaining performance. → Instruction tuning: Trains models to interpret and follow specific commands accurately. Perfect for workflow automation. → RLHF (reinforcement learning from human feedback): Models learn from human preferences and judgments, not just data patterns. When to add fine-tuning to your prompt engineering approach: → If you're in healthcare and need AI that understands medical terminology precisely → If you're in finance and regulatory compliance errors aren't acceptable → If you want consistent brand voice across thousands of AI interactions → If you need accuracy levels that require deeper domain knowledge The key is building a layered approach that matches your specific use case, data availability, and performance requirements. What factors are most important when you evaluate AI customization approaches? #ArtificialIntelligence #MachineLearning #AICustomization #FineTuning