Training Content Management Systems

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

Weekend Research Deep Dive #05 — AI-Enhanced XR for Learning & Training (2024–2025) Continuing the weekend series where I break down one high-value research area for builders, educators, and XR/AI practitioners. This week’s theme: How AI-driven personalization, adaptive feedback, and multimodal interaction are transforming XR learning from static experiences into responsive learning systems. 🔹 This week’s reads 1. Evaluating eXtended Reality (XR) and Desktop Modalities for AI Education   Feijoo-Garcia et al., 2025   https://lnkd.in/gEp5zHxx Shows that immersive XR environments outperform desktop learning for AI education in engagement and retention, highlighting the role of spatial interaction in deeper cognitive processing. 2. LLM-Based Adaptive Feedback in XR Learning   Gianni et al., 2025   https://lnkd.in/g78BBHpf Introduces an AI-driven XR framework that adapts feedback and difficulty in real time, improving learner motivation while raising important design and ethical considerations. 3. Multimodal Natural Interaction for Wearable XR   Wang, 2025   https://lnkd.in/gidn4zJ6 Reviews AI-enabled interaction methods such as gaze, gesture, and voice, showing how natural input expands immersion and reduces interaction friction in learning environments. 🔹 Why it’s worth your coffee AI + XR is moving beyond immersion toward adaptive learning systems. The research points to three key shifts: 1. Adaptive learning loops   XR systems increasingly adjust guidance, pacing, and difficulty based on learner behavior. 2. Cognitive-aware design   AI enables XR experiences that manage cognitive load instead of overwhelming users. 3. Measurable learning outcomes   Behavior traces and interaction data make skill progression observable and assessable. 3 takeaways for practitioners: • Start with pedagogy first — XR + AI delivers value only when aligned with clear learning objectives.   • Use multimodal interaction intentionally — gaze, gesture, and voice should simplify learning, not distract.   • Track learning outcomes alongside engagement — immersion alone does not guarantee understanding. Question for the community: If you were designing an AI-enhanced XR learning system today, where would you focus first? (A) AI-guided tutoring   (B) Adaptive difficulty & feedback   (C) Multimodal interaction   (D) Learning analytics & assessment #XR #AI #HCI #EdTech #ImmersiveLearning #SpatialComputing #Research

Target gives real-time feedback to their employees every 3 seconds. Every time a cashier scans an item, they see color-coded feedback on their screen: 🟢 Green = On pace 🟡 Yellow = Slightly behind 🔴 Red = Need to speed up After each transaction, they see their average speed (creating a personal benchmark). Studies from Alibaba's warehouses show real-time feedback improves efficiency by 7.0%, with notable gains across all performance levels.1 Gallup also found 80% of employees who receive meaningful weekly feedback are fully engaged, suggesting recency matters.2 The problem with traditional performance reviews is that by the time you tell someone they're off track, habits are already formed. They don't know what they're being rewarded for or what they should change. Real-time feedback removes the ambiguity. Workers adjust in the moment and their performance improves immediately. This doesn’t simply apply to cashiers though. Many frontline roles, from restaurant service to healthcare documentation to manufacturing, could benefit from clearer, immediate feedback. Setting clear goals and providing timely feedback, and tools that provide staff real-time coaching, equips them to succeed.

The consulting industry built a multi-billion dollar business on one premise. Sales methodologies are relatively easy to teach and almost impossible to adopt. That premise is no longer true. We are launching a new value selling methodology this quarter, and rather than writing a six or seven figure check to reinforce it, we are using the Otter.ai MCP server with Claude to do the reinforcement automatically. Every customer call gets scored in real time against our four-box framework, with additional ratings for multithreading and discovery depth. The scorecard posts into Slack within minutes of the call ending, complete with a rating per box, a written rationale, and the top three coaching moments including the exact language the rep could have used in the moment. The screenshot below is a real example from one of our own discovery calls, with names redacted. Think about what this actually replaces. The offsite training, the laminated cards, the CRM scorecards nobody fills in, the quarterly pipeline reviews where managers retroactively apply the framework to deals they half-remember, and the consulting partner checking on adoption every six weeks. All of it was a very expensive way to solve a reinforcement problem at scale, and agentic AI solves that problem natively. Reps get specific feedback tied to the exact moment they missed. Managers review coaching signal across dozens of calls in the time it used to take to review one. Leaders track longitudinal progression per competency for every rep in the org, in real time. The playbook for rolling out sales methodology has fundamentally changed, and the cost structure that came with it has changed right along with it. #aicoach #otter.ai #aiimpact

Reinforcement Learning from Human Feedback (RLHF) is transforming Large Language Models (LLMs) by establishing a feedback loop that significantly enhances their performance beyond traditional training methods. Unlike standard supervised learning, which relies on static datasets, RLHF integrates real-time human input into reinforcement signals, enabling LLMs to better grasp language subtleties and user intent. For example, OpenAI's ChatGPT continually refines its responses based on user interactions, resulting in more contextually relevant and user-aligned exchanges. This adaptive approach is crucial for bias mitigation, addressing harmful stereotypes related to race, gender, and other factors. When users identify biased outputs or provide corrective feedback, the model learns and improves continuously, reducing the risk of reinforcing these biases. Techniques such as Proximal Policy Optimization (PPO) help balance competing objectives, ensuring that LLMs not only foster creativity but also uphold factual accuracy. In creative writing, RLHF empowers models to generate imaginative content that resonates with readers while remaining grounded in realistic contexts. Future developments in RLHF may explore hybrid models that combine human feedback with automated systems, creating a scalable framework for refining LLM outputs. This evolution is essential for enhancing the quality of AI-generated content and promoting ethical standards, including fairness, accountability, and transparency. Ultimately, RLHF facilitates more effective machine-human interactions, allowing AI systems to adapt intuitively while prioritizing user trust and ethical responsibility. TL;DR: RLHF uses human feedback to make LLMs more accurate, ethical, and responsive to user intent. #RLHF #LLMs #AI #MultiObjectiveOptimization #Ethics #HumanFeedback #ScalableAI

Every struggling new hire carries “baggage” from their last job. They just need a reset, not a rejection. A new hire once froze in a meeting when I asked for their thoughts. Later, he admitted, "In my last job, only managers spoke. I wasn’t sure if I should." That’s when I realized you’re not just hiring a person. You’re hiring their past workplace norms too. I now use a 3-phase framework to spot, reset, and reinforce workplace norms early. Phase 1: Surface the hidden sensitivities New hires won’t tell you what’s confusing. They’ll just hesitate. I try to uncover what they assume is “normal.” I look for clues: 🔍 Do they wait for permission instead of taking initiative? 🔍 Do they avoid pushing back in discussions? 🔍 Are they hesitant to ask for feedback? You can do this with an easy expectation reset exercise in onboarding: 1. "At your last job, how did decisions get made?" 2. "How was feedback typically given?" 3. "What was considered ‘overstepping’?" Their answers reveal hidden mismatches between their old playbook and your culture. Phase 2: Reset & align Don’t assume new hires will "figure it out". Make things explicit. I set clear norms: 1. Here, we challenge ideas openly, regardless of role. 2. We give real-time feedback—don’t wait for formal reviews. 3. Speed matters more than waiting for perfection. For this, use “Culture in Action” moments. → Instead of just telling them, model it in real time. → If they hesitate to push back, directly invite them to challenge something. → If they overthink feedback, normalize quick iteration—not perfection. Phase 3: Reinforce through real work Old habits don’t vanish. They resurface under stress. The real test is how they act when things get tough. Create intentional pressure moments: 1. Put them in decision-making roles early. 2. Assign them a project where feedback loops are fast. 3. Push them to own a meeting or initiative. Post-action debriefs help here: “I noticed you held back in that discussion—what was going through your mind?” This helps them reflect & adjust quickly, instead of carrying misaligned habits forward. Most onboarding processes focus on training skills. But resetting unspoken norms is just as critical (if not more). A struggling new hire isn’t always a bad fit. Sometimes, they’re just following the wrong playbook. What’s a past habit you had to unlearn in a new job?

10,000 hours of practice? Yeah, they still matter, but they only pay off when each hour rides shotgun with immediate feedback. Stanford neuroscientist David Eagleman told Inc. Magazine that relevance and real-time correction are the multipliers that turn long practice into fast mastery. If practice is water, feedback is the cup that keeps it from spilling out all over the place. When repetition runs on autopilot, your brain quietly holds on to every flaw. A crisp critique, whether from a coach, a peer, or an AI copilot, snaps you back into conscious control. It rewires the pattern before it hardens, and delivers the small win that keeps motivation rolling for the next rep. Practical ways to blend those hours with high-velocity feedback: 🏹 Set micro-targets for every session Name one measurable outcome before you start (trim thirty seconds off a 5K split, refactor a function to cut runtime by five percent, open a discovery call without filler words). End only after you check that metric. 🏹 Build a same-day feedback channel Pair each practice block with a critic who can respond within twenty-four hours: a mentor dropping Loom notes on your sales call, an AI pair-programmer flagging inefficient loops the moment you hit Save, or a training app overlaying bike-fit angles on video right after your ride. 🏹 Run a five-minute post-mortem Immediately jot what worked, what flopped, and the single tweak you will test next time. Reflection turns raw data into insight while the memory is still warm. 🏹 Track velocity over volume Count iterations per week, bugs squashed per hour, objections neutralized per call, or whatever. Share those numbers publicly so the team celebrates speed of improvement rather than brute hours logged. If 10,000 hours is tuition, feedback is the scholarship that lets you graduate early. Which feedback ritual shaved months off your learning curve? Share so we can tighten the loop together. Welcome to Tuesday, ya'll!

I watched a startup blow $150K on a 3-day sales training last quarter. Within 24 hours, their reps had forgotten 70% of what they'd learned. Here's what happened: They flew the entire sales team to a retreat for a 3-day SKO. Brought in expensive trainers. Created elaborate role-playing scenarios. Everyone left pumped up and ready to crush their numbers. Three weeks later, I sat in on their pipeline review. Reps couldn't remember basic product positioning. They were still using outdated talk tracks. The training investment had evaporated. The hard truth: Traditional sales training doesn't stick because it's disconnected from real selling moments. Your brain forgets classroom content within hours. Product knowledge decays fastest. Even expensive LMS platforms with hour-long modules get gamed by reps who speed through at 2x while multitasking. The breakthrough happens when training meets real-time selling. Instead of quarterly offsites, successful teams should deploy AI-powered training that activates during actual sales motions. Pre-call refreshers generated from your CRM data. Real-time battlecards that surface during discovery calls. Win-loss analysis that feeds back into coaching within hours, not months. When training happens in the moment of need, you close the feedback loop in real-time allowing sales teams to apply what they learn on the fly. When you do this your training investment actually compounds instead of evaporating. And those expensive SKOs? We should praise them for what they really are: motivational team building rallies to pump up the sales team for the next quarter. #SalesEnablement #Sales #Training

🎤 Using AI to improve soft skills I wrote recently about soft skills becoming more valuable as AI handles more technical work. Communication, persuasion, presence: these aren't going away. What fascinates me is how AI can help us get better at them. A big part of my job is giving talks. Conferences, seminars, lectures, grand rounds, panels. And despite decades of practice, I still catch myself saying "you know" much more than I'd like and racing through slides. Just a bad habit. So I built something to help. 🛠️ The tool I built an iOS app that listens while I speak, even during actual talks. It catches filler words (um, uh, like, you know) and tracks my speaking pace in real time. Discreet enough to run during a lecture, useful enough to change behavior. It also confirmed what I suspected: I speak at 200+ WPM when the ideal is 150-170. Why Deepgram? Two reasons. First, it's fast, sub-300ms latency, which matters when you want real-time feedback. Second, its nova-2 model has native filler word detection. Most speech-to-text systems clean these out to make transcripts prettier. But when you're trying to improve, you want the messy version with every verbal stumble preserved. 💡 Why this matters beyond my own speaking Voice APIs have reached a tipping point. What required a team of engineers five years ago is now a weekend project. Deepgram, ElevenLabs, OpenAI's Realtime API, all offer streaming capabilities with sub-second latency and surprisingly affordable pricing. I think we're about to see an explosion of personal AI coaches. Not just for speaking, for writing, for coding, for any skill where real-time feedback accelerates learning. The infrastructure is ready. (The video below is deliberately exaggerated to demo the tool. I don't actually quite talk like that. Usually 😀 )

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝘂𝗹𝗱 𝗴𝗲𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗼𝗻𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁? 𝗡𝗼𝘁 𝗮𝗳𝘁𝗲𝗿 𝗮 𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲. 𝗡𝗼𝘁 𝗮𝗳𝘁𝗲𝗿 𝗮 𝗱𝗲𝘃 𝘁𝗲𝗮𝗺 𝗽𝘂𝘀𝗵𝗲𝘀 𝗮 𝗳𝗶𝘅. 𝗥𝗶𝗴𝗵𝘁 𝗻𝗼𝘄. 𝗜𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲. That's online self-improvement. The agent produces an output, evaluates it, and revises its approach - all while it's still running. No human in the loop. No downtime. Sounds ideal, right? Here's where it gets tricky. Real-time adaptation is powerful but fragile. If the feedback signal is noisy - one angry user, one edge case - the agent can overcorrect. It starts optimizing for the last conversation instead of the average conversation. We've seen this with our customers at Noveum AI. An agent that performed well across 500 calls suddenly degrades because it adapted too aggressively to a handful of outlier interactions. Online learning works best when it's bounded. Self-reflection loops, step-level verification, confidence thresholds - these are the guardrails that keep real-time adaptation from becoming real-time drift. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: 𝗼𝗻𝗹𝗶𝗻𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘀𝗽𝗲𝗲𝗱. 𝗕𝘂𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝘀𝗽𝗲𝗲𝗱 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Next post - the other side: Offline Self-Improvement.