AI Lead Response Confidence Thresholds

Should you blindly trust AI? Most teams make a critical mistake with AI - we accept its answers without question, especially when it seems so sure. But AI confidence ≠ human confidence. Here’s what happened: The AI system flagged a case of a rare autoimmune disorder. The doctor, trusting the result, recommended an aggressive treatment plan. But something felt off. When I was called in to review, we discovered the AI had misinterpreted an MRI anomaly. The patient had a completely different condition - one that didn't require that aggressive treatment. One wrong decision, based on misplaced trust, could’ve caused real harm. To prevent this amid the integration of AI into the workforce, I built the “acceptability threshold” framework. Here’s how it works: This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 1. Measure how accurate humans are at a task (our doctors were 93% accurate on CT scans) 2. Use that as our minimum threshold for AI. 3. If AI's confidence falls below this human benchmark, a person reviews it. This approach transformed our implementation and prevented future mistakes. The best AI systems don't replace humans - they know when to ask for human help. What assumptions about AI might be putting your projects at risk?

I’m jealous of AI Because with a model you can measure confidence Imagine you could do that as a human? Measure how close or far off you are? here's how to measure for technical and non-technical teams For business teams: Run a ‘known answers’ test. Give the model questions or tasks where you already know the answer. Think of it like a QA test for logic. If it can't pass here, it's not ready to run wild in your stack. Ask for confidence directly. Prompt it: “How sure are you about that answer on a scale of 1-10?” Then: “Why might this be wrong?” You'll surface uncertainty the model won't reveal unless asked. Check consistency. Phrase the same request five different ways. Is it giving stable answers? If not, revisit the product strategy for the llm Force reasoning. Use prompts like “Show step-by-step how you got this result.” This lets you audit the logic, not just the output. Great for strategy, legal, and product decisions. For technical teams: Use the softmax output to get predicted probabilities. Example: Model says “fraud” with 92% probability. Use entropy to spot uncertainty. High entropy = low confidence. (Shannon entropy: −∑p log p) Language models Extract token-level log-likelihoods from the model if you have API or model access. These give you the probability of each word generated. Use sequence likelihood to rank alternate responses. Common in RAG and search-ranking setups. For uncertainty estimates, try: Monte Carlo Dropout: Run the same input multiple times with dropout on. Compare outputs. High variance = low confidence. Ensemble models: Aggregate predictions from several models to smooth confidence. Calibration testing: Use a reliability diagram to check if predicted probabilities match actual outcomes. Use Expected Calibration Error (ECE) as a metric. Good models should show that 80% confident = ~80% correct. How to improve confidence (and make it trustworthy) Label smoothing during training Prevents overconfident predictions and improves generalization. Temperature tuning (post-hoc) Adjusts the softmax sharpness to better align confidence and accuracy. Temperature < 1 → sharper, more confident Temperature > 1 → more cautious, less spiky predictions Fine-tuning on domain-specific data Shrinks uncertainty and reduces hedging in model output. Especially effective for LLMs that need to be assertive in narrow domains (legal, medicine, strategy). Use focal loss for noisy or imbalanced datasets. It down-weights easy examples and forces the model to pay attention to harder cases, which tightens confidence on the edge cases. Reinforcement learning from human feedback (RLHF) Aligns the model's reward with correct and confident reasoning. Bottom line: A confident model isn't just better - it's safer, cheaper, and easier to debug. If you’re building workflows or products that rely on AI, but you’re not measuring model confidence, you’re guessing. #AI #ML #LLM #MachineLearning #AIConfidence #RLHF #ModelCalibration

I built a prompt that makes AI stop guessing and start thinking more carefully I created something called the Accuracy-First Assistant Protocol, a way to help AI focus on being correct rather than just sounding confident. 🔗 Download the protocol: https://lnkd.in/ehkA2kCM 🔧 The problem Most AI models today are designed to: → sound natural → be helpful → keep the conversation flowing But they’re not designed to always be right. That’s why they sometimes → make things up (hallucinate) → fill in missing details → sound confident even when unsure ⚙️ What this prompt does Instead of changing the AI model itself, this works at the instruction level. It gives the AI a strict set of rules that it must follow every time it answers. In simple terms, it forces the AI to: 1. Show how confident it is The AI must say whether it’s highly confident, somewhat confident, or unsure. 2. Stop guessing If information is missing, it must say so, not try to “fill in the blanks". 3. Check it before answering Before giving a final answer, it has to: → confirm that they understood the question → point out missing information → explain how reliable the answer is → say where the answer comes from 🧠 Why this works AI models are very sensitive to instructions. When you give them: → clear rules → repeated constraints → a fixed response structure …you can actually change how they behave without retraining them. Instead of jumping straight to an answer, the AI slows down and becomes more careful. 📊 What changes in practice You’ll notice: ✔ Less made-up information ✔ More honest answers ✔ Clearer explanation of uncertainty ✔ Better quality responses overall Trade-offs: → Slightly slower responses → More structured (less conversational) → Sometimes asks for more info before answering 🧩 What this really is This is a simple form of 👉 Controlling AI behaviour through prompts → No fine-tuning. → No extra tools. → Just better instructions. 🚀 The bigger idea We’re moving from: “Using AI tools” to design how AI behaves The real skill isn’t just using AI anymore… It’s knowing how to guide it. Curious what you think: Would you rather have AI that’s fast and confident or slower but more honest? ***Follow me for insights and practical tips*** #AI #ArtificialIntelligence #PromptEngineering #LLM #MachineLearning #GenerativeAI #AIEngineering #Tech #Innovation #AIAlignment #FutureOfWork

𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁? Configuration management in aerospace assumes deterministic systems. A configuration item either matches its approved baseline or it doesn't. A change is either authorized or it isn't. This binary certainty enables governance frameworks that make CM valuable for safety-critical systems. Machine learning operates probabilistically. It assigns confidence levels, not certainties. An algorithm identifies duplicate records with 87% confidence. Neural networks predict the impact of change with 76% likelihood. One global process manufacturer reported that “AI surfaced more than 3,000 duplicate materials and highlighted 2,200 items at stockout risk.” “Result: 21 million dollars in verified savings, outages reduced from 4 plus weeks to 3 days, and unified visibility across plants.” Yet this reveals the challenge: the AI system assigns confidence scores ranging from 62% to 99.8%. The manufacturer established an 85% threshold requiring manual review below that level, but this was organizational policy, not guidance from configuration management standards. SAE EIA-649, the configuration management standard adopted by the U.S. Department of Defense, defines five core functions without constraining implementation. This flexibility permits AI adoption but provides no framework for validating probabilistic outputs against deterministic compliance requirements. When your PLM system's AI-based data classification automatically categorizes product data, what confidence threshold triggers manual review? At what precision level does automatically identified traceability become trustworthy for audit purposes? Traditional configuration audits check baselines through manual inspection or automated inventory comparison. When AI uses computer vision to verify physical assemblies against digital models, the output includes confidence intervals and edge-case ambiguity. A 97% confidence that an assembly matches its baseline might be excellent for screening, but is it sufficient for regulatory compliance? Industry has pragmatically adopted AI where business value exceeds implementation risk, but current standards provide no framework for incorporating probabilistic confidence measures into configuration decisions. What's missing isn't better algorithms, it's governance frameworks that specify acceptable error rates, validation requirements for probabilistic decision support, and audit approaches for systems that combine human and machine judgment. When your AI flags a change impact with 73% confidence in an avionics system, and your approval process demands binary yes/no decisions, whose judgment determines whether 73% is adequate, and what happens when that judgment is wrong? What confidence thresholds has your organization established for AI-assisted configuration decisions? #ConfigurationManagement #CM2 #ArtificialIntelligence #RiskManagement #PLM #AI #ProductLifecycleManagement