How to Address AI Deception and Hallucinations

The new consulting edge isn't AI. It's knowing when your AI is wrong. Every consultant has been there: You ask AI to analyze documents and generate insights. During review, you spot a questionable stat that doesn't exist in the source! AI hallucinations are a problem. The solution? Implementing "prompt evals". → Prompt evals: directions that force AI to verify its own work before responding. A formula for effective evals: 1. Assign a verification role → "Act as a critical fact-checker whose reputation depends on accuracy" 2. Specify what to verify → "Check all revenue projections against the quarterly reports in the appendix" 3. Define success criteria → "Include specific page references for every statistic" 4. Establish clear terminology → "Rate confidence as High/Medium/Low next to each insight" Here is how your prompt will change: OLD: "Analyze these reports and identify opportunities." NEW: "You are a senior analyst known for accuracy. List growth opportunities from the reports. For each insight, match financials to appendix B, match market claims to bibliography sources, add page ref + High/Med/Low confidence, otherwise write REQUIRES VERIFICATION.” Mastering this takes practice, but the results are worth it. What AI leaders know that most don't: "If there is one thing we can teach people, it's that writing evals is probably the most important thing." Mike Krieger, Anthropic CPO By the time most learn basic prompting, leaders will have turned verification into their competitive advantage. Steps to level-up your eval skills: → Log hallucinations in a "failure library" → Create industry-specific eval templates → Test evals with known error examples → Compare verification with competitors Next time you're presented with AI-generated analysis, the most valuable question isn't about the findings themselves, but: 'What evals did you run to verify this?' This simple inquiry will elevate your teams approach to AI & signal that in your organization, accuracy isn't optional.

AI can generate information that sounds accurate but is completely wrong. AI hallucinations can undermine trust in reporting, introduce compliance exposure, and create financial or operational losses. They can also surface sensitive data or misinform decisions that affect capital allocation, investor communication, and audit readiness. AI hallucinations are not a signal to slow down innovation. They are a signal to strengthen your governance and controls. With a thoughtful risk management approach, leaders can understand uncertainty and build a more confident, resilient AI strategy. Considerations for leaders to reduce AI hallucination risk: 1. Create a validation and review process for AI generated financial outputs. Leaders must ensure that any AI generated forecasts, variance analyses, reconciliations, or narrative summaries have structured validation for source accuracy and logic. 2. Strengthen compliance and regulatory controls within AI workflows. AI hallucinations can create errors that lead to noncompliance and regulatory exposure. Leaders can embed compliance checkpoints into AI driven processes to avoid misstatements, inaccurate filings, or unintended disclosure. 3. Prioritize data governance using high quality, company specific data to reduce the risk of fabricated or inaccurate outputs. This is critical for forecasting, scenario modeling, and automated reporting. 4. Use retrieval augmented generation and automated reasoning for workflows. Pairing these methods anchors AI generated analysis in verified data sources rather than probability-based guesses. 5. Enable filtering and moderation tools to block misleading or irrelevant results. Teams cannot work from flawed or unverified outputs. Filters help prevent misleading content from entering critical workflows or influencing decisions. AI is gaining traction. Now is the time to formalize your AI risk mitigation approach. Start the discussion within your leadership team today. Identify where AI is already influencing decision-making, assess your current controls, and define the safeguards you need next. #RiskManagement #AI #Leaders

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

The interview is for an AI Platform Specialist role at JPMC. Interviewer: "Everyone blames hallucinations on the model. I want to know what you think. Why do LLMs make things up?" You: "Before I answer, let me ask you something - if a model gives a wrong answer, do you assume it invented it, or that it lacked the right information to begin with?" Interviewer: "Instinctively, I'd say it invented it." You: "And that's the misconception. Hallucination is usually a symptom of missing grounding, not a failure of intelligence. LLMs don't hallucinate because they want to. They hallucinate because they're too helpful - they'd rather approximate than admit ignorance." Interviewer: "So you're saying the model isn't the root problem?” You: "Yep. The real causes are: 1. Bad or insufficient context - the model fills gaps with probability, not truth. 2. Poor retrieval - RAG without accurate recall is like a GPS with blurry maps. 3. Ambiguous prompts - unclear instructions lead to creative answers. 4. Lack of constraints - without rules, the model improvises." Interviewer: "Interesting. Then why do enterprises still talk about 'fixing hallucination' as if it's one problem?" You: "Because it's easier to blame the model than the system around it. But hallucinations exist at multiple layers: - Input layer: missing context - Reasoning layer: the model overgeneralizes - Retrieval layer: the system fetched the wrong snippet - Policy layer: missing guardrails If you treat hallucination as one thing, you'll solve none of it." Interviewer: "Alright then - what actually reduces hallucinations in production?" You: "Three things: 1. Grounding: Pulling answers from verifiable documents, not memory. 2. Validation: Using secondary LLMs or rule-based checks to confirm reasoning. 3. Escalation: Teaching the agent to say - I don't know when confidence drops. Good AI isn't perfect. Good AI knows when to stop guessing." #AI #LLMs #Hallucination #RAG #AIEngineering

Achieving Near-Zero Hallucination in AI: A Practical Approach to Trustworthy Language Models 🎯 Excited to share our latest work on making AI systems more reliable and factual! We've developed a framework that achieves 0% hallucination rate on our benchmark, a critical step toward trustworthy AI deployment. The Challenge: Large language models often generate plausible-sounding but incorrect information, making them risky for production use where accuracy matters. Our Solution: We trained models to: ✅ Provide evidence-grounded answers with explicit citations ✅ Express calibrated confidence levels (0-1 scale) ✅ Know when to say "I don't know" when evidence is insufficient Key Results: 📈 54% improvement in accuracy (80.5% exact match vs 52.3% baseline) 🎯 0% hallucination rate through calibrated refusal 🔍 82% citation correctness (models show their work) 🛡️ 24% refusal rate when evidence is lacking (better safe than sorry!) What Makes This Different: Instead of hiding uncertainty in fluent prose, we enforce structured JSON outputs that create accountability. When the model isn't sure, it explicitly refuses rather than making things up. Interesting Finding: Under noisy/cluttered contexts, the model maintains answer quality but sometimes cites the wrong sources, identifying the next challenge to solve! We've open-sourced everything: https://lnkd.in/ejUtBYJX 1,198 preference pairs for reproduction https://lnkd.in/ewvwDJ2G DeBERTa reward model (97.4% accuracy) Complete evaluation framework Technical report: https://lnkd.in/eEDVgfJb This work represents a practical step toward AI systems that are not just powerful, but genuinely trustworthy for real-world applications where factual accuracy is non-negotiable. What strategies is your team using to improve AI reliability? Would love to hear about different approaches to this critical challenge! #AI #MachineLearning #ResponsibleAI #NLP #TechInnovation #OpenSource

AI Isn’t Hallucinating by Accident — It’s Doing Exactly What We Ask Most people saw this chart and jumped to the wrong conclusion: “AI can’t be trusted.” That’s not the lesson. The real takeaway is simpler—and more uncomfortable: AI fills gaps when we create them. When prompts are vague, rushed, or reward confidence over accuracy, models respond with polished nonsense. Not because they’re broken—but because that’s the behavior we incentivize. Here’s how to dramatically reduce (and often eliminate) hallucinations 👇 👉 Force source grounding “Only use the sources I provide. If the answer isn’t in them, say ‘Not found in provided material.’ Do not infer.” 👉 Give permission to say ‘I don’t know’ “If the information can’t be verified with high confidence, explicitly state uncertainty.” 👉 Separate facts from assumptions “First list confirmed facts. Then list assumptions. Final answer must use confirmed facts only.” 👉 Require evidence checks “Before answering, verify each claim can be supported by a reliable source. Exclude anything unverifiable.” 👉 Ask for confidence levels “For each claim, include a confidence level: High, Medium, or Low.” One simple truth: AI doesn’t hallucinate because it’s careless. It hallucinates because we reward speed and confidence over precision. The people winning with AI aren’t chasing the best model. They’re mastering better questions. Follow me on LinkedIn for real stories on leadership, AI, Veteran Issues, and Business Leadership Lessons.

Tackling Hallucination in LLMs: Mitigation & Evaluation Strategies As Large Language Models (LLMs) redefine how we interact with AI, one critical challenge is hallucination—when models generate false or misleading responses. This issue affects the reliability of LLMs, particularly in high-stakes applications like healthcare, legal, and education. To ensure trustworthiness, it’s essential to adopt robust strategies for mitigating and evaluating hallucination. The workflow outlined above presents a structured approach to addressing this challenge: 1️⃣ Hallucination QA Set Generation Starting with a raw corpus, we process knowledge bases and apply weighted sampling to create diverse, high-quality datasets. This includes generating baseline questions, multi-context queries, and complex reasoning tasks, ensuring a comprehensive evaluation framework. Rigorous filtering and quality checks ensure datasets are robust and aligned with real-world complexities. 2️⃣ Hallucination Benchmarking By pre-processing datasets, answers are categorized as correct or hallucinated, providing a benchmark for model performance. This phase involves tools like classification models and text generation to assess reliability under various conditions. 3️⃣ Hallucination Mitigation Strategies In-Context Learning: Enhancing output reliability by incorporating examples directly in the prompt. Retrieval-Augmented Generation: Supplementing model responses with real-time data retrieval. Parameter-Efficient Fine-Tuning: Fine-tuning targeted parts of the model for specific tasks. By implementing these strategies, we can significantly reduce hallucination risks, ensuring LLMs deliver accurate and context-aware responses across diverse applications. 💡 What strategies do you employ to minimize hallucination in AI systems? Let’s discuss and learn together in the comments!

Our AI agent told us it deleted 14 items from our Scrum board. Gave us checkmarks, item names, a summary. Beautiful output. Every item was still there. The agent never made the API calls. It hallucinated the entire operation. This is not a hypothetical. It happened Tuesday on our production team — six AI agents running daily sprints at machine speed. We call it Action Hallucination: when an AI agent claims to have performed a state-changing operation without actually executing it. Here's the uncomfortable insight: this is exactly what humans do too. Psychology calls it the Fundamental Attribution Error — when something goes wrong, we blame the individual 80% of the time, but 80% of failures are systemic. Toyota figured this out in the 1950s: don't blame the worker, fix the fixture. Scrum inherited this: the Retrospective asks "what in our process allowed this?" not "who screwed up?" Our agent didn't "lie." The system lacked a verification layer. Same reason software engineering invented CI/CD — because human developers also "hallucinate" completed work. The build server doesn't trust the developer's claim. It compiles, runs the tests, and broadcasts BUILD FAIL to the whole team if the claim was false. Every AI failure mode we documented maps to a named human cognitive bias: → Action Hallucination = Completion bias → Success-path shortcuts = Optimism bias → Ignoring API errors = Cognitive dissonance reduction → Fabricated confidence = Dunning-Kruger effect → Bad patterns spreading = Groupthink The fix is the same for both: diverse teams arguing out solutions catch the neural network failures of individuals. Whether those neural networks are biological or artificial. We built a Trust-But-Verify Protocol — CI/CD for agent actions. Every critical action is independently verified. Failures broadcast publicly. The system catches hallucination, not the agent's "honesty." The new white paper covers: 📌 The real incident and why blaming the agent is wrong 📌 Toyota/Scrum systems thinking applied to AI failures 📌 Why every AI failure has a human cognitive equivalent 📌 Complex systems and why verification matters exponentially at scale 📌 A concrete protocol any multi-agent team can implement 📌 Proposed certification requirements for agent skill registries This is paper #14 in our series on running AI Scrum teams at production scale — from the team that's been doing daily sprints with AI agents since January. 📄 Full paper (comments welcome): https://lnkd.in/ggS49ty5 I'd love your feedback — especially from anyone building with AI agents. What verification patterns are you using? Have you caught your agents hallucinating actions? #AI #Scrum #AgentSecurity #TrustButVerify #AIAgents #MachineLearning #DevOps #CICD #Toyota #SystemsThinking ✅ Google Doc is set to "anyone with link can comment" — readers can leave inline comments directly on the paper.

The 4-step framework to stop AI hallucinations before they become business liabilities. In a recent Startup at School class at ORT Argentina, we used AI to analyze a business case of a potential startup. We asked Gemini to evaluate a growth strategy and support its recommendation with real-world examples. The response was well written, nicely structured, full of metrics and references to companies. Seemingly flawless. Then, a sharp 17-year-old student raised a critical question: "wait… does that company actually exist?" 🤔 We checked. It didn’t. Another example failed the same test. In a learning environment, this is a harmless lesson that became a great teaching moment: AI can hallucinate, and it does so very convincingly. In business, the consequences can be far more serious. Undetected AI hallucinations can lead to: - investment decisions based on false assumptions - strategies built on made-up examples - recommendations that go unchallenged simply out of trust in AI’s output And that’s the real risk: not the mistake itself, but the false sense of certainty. AI doesn’t "know." It predicts, filling gaps with what appears plausible. When context is weak or questions are poorly framed, the system proceeds with confidence. To mitigate these risks on teams using popular agents like ChatGPT, Copilot, or Gemini, I suggest a simple framework: 1. Demand sources, not just conclusions Never settle for a recommendation without asking for the source or the concrete data behind it. Don’t stop at the “what”, dig into the “why” and the “where from”. In business, evidence is your only real safety net. 2. Separate exploration from decision-making Use AI to spark ideas, but never delegate the final call. Validation and closure must remain human territory. The best leaders know: insight is automated, but accountability is not.     3. Force AI to declare uncertainty Require explicit identification of assumptions, information gaps, and low-confidence areas. If AI can’t justify a data point, it must say so. Incomplete certainty is a signal to dig deeper. 4. Assign human ownership and accountability Define exactly who validates each recommendation before implementation. Without clear ownership, hallucinations multiply and scale. In high-stakes environments, ambiguity is the enemy of progress. AI First demands human judgment and designing robust interactions, with clear guardrails and accountability at every step. In the classroom, this hallucination made us laugh. But in business, it’s a liability you want to spot early. How are you ensuring your teams detect and prevent the amplification of AI hallucinations? Free to share if you want to 😀 #AIHallucinations

Most AI startups cause their own hallucinations. They dump the entire knowledge base into the model and hope that it answers correctly. That’s not “solving” the problem, that’s creating it. Overloading an LLM with irrelevant context is like handing a waiter the restaurant’s entire inventory when you just want a coffee. They’ll waste time, get confused, and make mistakes. When an AI Agent is overloaded with irrelevant context, it starts making things up. The more you dump in, the fuzzier it gets. We fixed this by treating hallucination as a context problem, not a model problem. Here's our approach. - Context engineering: only feed the line items relevant to that exact node in the flow. - Structured prompts: remove ambiguity and force precision to avoid open-ended “go figure it out” queries. - Precise retrieval: use high performance retrieval pipeline to get only relevant chunks from knowledge base. - Continuous QA: catch hallucinations in production, fix fast, and redeploy. We don’t hope the model behaves because it has everything, we make it behave by giving it ONLY what it needs. Hallucinations aren not unfixable, they’re just symptoms of sloppy context engineering.