Evaluating the Accuracy of AI-Generated Insights

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.

During the last few weeks, I have spoken with many UX colleagues about their concerns regarding the use of AI. The two issues that consistently come up are hallucination and inconsistency. People worry that one model produces one set of themes, another model generates slightly different conclusions, and suddenly the analysis feels unstable and unreliable. These concerns are valid, however I believe they are partially manageable. Hallucination often happens when a model is asked to generate insights without grounding in actual data. One of the most effective ways to reduce this risk is using Retrieval Augmented Generation, or RAG. Instead of allowing the model to rely on its general training patterns, RAG forces it to retrieve relevant interview segments first and then generate insights only from those retrieved passages. When every theme must be anchored to specific verbatims, unsupported claims become far less likely. Inconsistency across models does not necessarily indicate failure. In fact, it can be used strategically. In traditional qualitative research, we rely on multiple human coders. We assess agreement, examine disagreement, and refine our categories accordingly. The same logic can be applied to AI. Running two different models in parallel for thematic analysis acts as a form of inter rater reliability. Each model independently extracts themes grounded in retrieved evidence. Then we compare them. Do they converge on similar clusters? Do they reference overlapping verbatims? Do they assign similar structural roles to the same behavioral patterns? When both models converge, confidence increases. When they diverge, that signals ambiguity, boundary issues, or data complexity. Disagreement becomes a diagnostic signal rather than a weakness. This is where Bayesian analysis adds another layer of rigor. Instead of stopping at agreement percentages, we can formally quantify uncertainty. We can estimate the posterior probability that a theme is truly prevalent given evidence from multiple models. We can model how strongly certain themes predict outcomes such as churn intention or satisfaction. We can update those probabilities as more interviews are collected. Rather than saying a theme appears important, we can estimate how likely it is to dominate across segments with credible intervals that reflect uncertainty. 1-AI provides scale and pattern detection. 2-RAG provides grounding and traceability. 3-Parallel models provide triangulation. 4-Bayesian analysis provides formal uncertainty modeling. When these components are combined thoughtfully, qualitative AI analysis shifts from a fragile black box to a structured probabilistic system. The real transformation is not about using AI faster. It is about designing AI workflows that are auditable, triangulated, and statistically grounded.

Ask Claude what Ai is good for, it will tell you summarizing user research. BUt if you ask if that's a good idea (based on research) it tells another story. Claude: Based on the research, GenAI's accuracy for summarizing user research is mixed at best. Here's what the evidence shows: Accuracy Issues Hallucination rates for summarization range from 3% to 26% depending on the model. A clinical study found a 1.47% hallucination rate and 3.45% omission rate for LLMs in medical note generation - relatively low but still concerning for critical applications. Most LLMs overgeneralize scientific results in 26-73% of cases when summarizing research, broadening conclusions beyond what the original text supports. This is particularly problematic for user research where nuance matters. Evaluation Challenges There are no fixed "expected results" for GenAI summarization, only results from an acceptable range. Common metrics include: ROUGE: Measures n-gram overlap but often fails to capture semantic meaning BERTScore: Better at semantic similarity but can overemphasize similarity while missing coherence and factual accuracy G-Eval: Uses LLMs as judges, which can create bias toward LLM-generated text over human-written content Practical Performance Experienced UX researchers find GenAI requires constant error-catching and is best used by experts, not beginners. One researcher testing ChatGPT for interview analysis found it useful as a research assistant with proper oversight but couldn't replace human analysis. An empirical study using LLMs for deductive coding of interview transcripts showed variable results across 160 iterations, highlighting inconsistency issues. Bottom Line GenAI can speed up initial analysis but: Accuracy varies wildly between models and tasks Requires experienced researchers to catch errors Works better for structured tasks than nuanced interpretation Should never be used without human verification The technology isn't reliable enough for autonomous user research summarization. It's a flawed assistant at best, requiring significant human oversight to produce trustworthy results.

I invited 31 researchers to test AI research synthesis by running the exact same prompt. They learned LLM analysis is overhyped, but evaluating it is something you can do yourself. Last month I ran an #AI for #userresearch workshop with Rosenfeld Media.  Our first cohort was full of smart, thoughtful researchers (if you participated in the workshop, I hope you’ll tag yourself and weigh in in the comments!). A major limitation of a lot of AI for UXR “thought leadership” right now is that too much of it is anecdotal: researchers run datasets a few times through a commercial tool and decide whether or not the output is good enough based on only a handful of results. But for nondeterministic systems like generative AI, repeated testing under controlled conditions is the only way to know how well they actually work. So that’s what we did in the workshop. Our workshop participants produced a lot of interesting findings about qualitative research synthesis with AI: 1️⃣ LLMs can product vastly different output even with the exact same prompt and data. The number of themes alone ranged from 5 to 18, with a median of about 10.5. 2️⃣ Our AI-generated themes mapped pretty well to human-generated themes, but there were some notable differences. This led to a discussion of whether mapping to human themes is even the right metric to use to evaluate AI synthesis (how are we evaluating whether the human-generated themes were right in the first place?). 3️⃣ The bigger concern for the researchers in the workshop was the lack of supporting evidence for themes. The supporting quotes the LLM provided looked okay superficially, but on closer investigation *every single participant* found examples of data being misquoted or entirely fabricated. One person commented that validating the output was ultimately more work than performing the analysis themselves. Now, I want to acknowledge that this is one dataset, one prompt (although, a carefully vetted one, written by an industry expert), and one model (GPT 4o 2024-11-20). Some researchers claim that GPT 4o is worse for research hallucinations–and perhaps it is–but it is still a heavily utilized model in current off-the-shelf AI research tools (and if you’re using off-the-shelf tools, you won’t always know which models they’re using unless you read a whole lot of fine print). But the point is–I think this is exactly the level at which we should be scrutinizing the output of *all* LLMs in research. AI absolutely has its place in the modern researcher’s toolkit. But until we systematically evaluate its strengths and weaknesses, we're rolling the dice every time we use it. We'll be running a second round of my workshop in June as part of Rosenfeld Media’s Designing with AI conference (ticket prices go up tomorrow; register with code PAINE-DWAI2025 for a discount). Or, to hear about other upcoming workshops and events from me, sign up for my mailing list (links below).

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

In this image, one of the widely used tools that claims to detect AI-generated text reports a 99.99% AI probability.The text being analyzed, however, is a passage from the U.S. Constitution, written in the 18th century.This is not an isolated incident. Research and expert reviews have shown that most AI-detection tools rely on surface-level statistical and linguistic patterns (such as sentence structure, coherence, and predictability), rather than on any scientifically reliable method capable of identifying the true origin of a text. As a result, these tools suffer from high false-positive rates, especially when evaluating well-written, structured, or academic content.The practical implication is troubling:the better and clearer the writing, the more likely it is to be wrongly labeled as “AI-generated.

As far as I know, Listen Labs is the only research platform using Krippendorff's Alpha to consistently measure AI accuracy and hit above the 0.8+ research standard threshold.   Most AI research platforms can't tell you how accurate their analysis actually is. And when you're making million-dollar decisions based on insights, that's a problem.   Krippendorff's Alpha is a statistical measure that tracks how consistently open-ended responses get coded.   Here's how it works: Human researchers code a set of open-ended responses. Our AI codes the same responses. We compare the results to see how closely they match.   The research standard threshold is 0.8+. We've tuned our model to consistently hit above that. (For context: Medical AI requires >0.80 before releasing datasets.)   When you're evaluating research platforms, you need to know: • Exactly what "accurate" means • How it's measured (compared to human researcher ground truth) • Where to be cautious (when results fall below threshold)   Next time you evaluate a research platform, ask: How do you validate your AI accuracy? If they can't answer, you're buying a black box.

You're three months into an AI implementation. Productivity metrics look great. Users are enthusiastic. Then someone discovers the AI has been confidently delivering incorrect information to clients. The technology worked exactly as designed, but the humans didn't know what to check for. I almost killed my intelligence automation project in week two. The script was generating beautiful weekly reports — well-formatted, insightful summaries, impressive source lists. Everything looked perfect until I started clicking links. Half led to 404 errors. A quarter were paywalled content I couldn't access. Several went to articles that sounded amazing but simply didn't exist. The AI had created plausible-sounding titles for books and papers that were never written. If I hadn't been the end user — if I'd delegated this to someone else to build and just consumed the outputs — I would have been citing fiction in my book proposal. Credibility destroyed. Months of work wasted. Here's what this taught me about enterprise AI risk: The failure wasn't obvious. It required domain knowledge to spot. The AI didn't flag its uncertainty — it presented hallucinations with the same confidence as real sources. Most organizations are implementing AI without anyone in the decision-making chain having experienced this failure mode personally. They're writing governance policies for risks they haven't felt. That's backwards. You can't govern what you don't understand. You can't anticipate failure modes you've never encountered. You can't ask the right questions of vendors if you don't know what breaks. The C-suite leaders who have personally hit an AI limitation, debugged a prompt, or discovered a hallucination will build fundamentally different safeguards than those relying on theoretical understanding. Here's what to think about this week: • Where might your AI systems be confidently wrong without anyone noticing? • Who in your organization has the domain expertise to spot AI-generated plausibility versus accuracy? • What would change about your AI governance if you'd personally experienced a significant AI failure? If this resonates with challenges your team faces, share it with them. Sometimes an outside voice opens stuck conversations about what we're not checking for. #AI #technology #leadership #changemakers #governance

40% Reddit. 21% Yelp. 39% confidence. 0% expertise. This creates a massive AI blind spot that most people miss: Large language models learn from user-generated content across Reddit, Wikipedia, YouTube, Facebook, and Yelp. The problem is fundamental. AI compresses the internet. But user-generated content isn't always expertise. 1/ The Hidden Risks: ↳ Minority opinions appear as majority consensus. ↳ Confidence gets mistaken for credibility. ↳ Popularity masquerades as truth. ↳ Random opinions carry equal weight with expert analysis. 2/ What This Means in Practice: After deploying AI for various organizations, I see this repeatedly. The most confident response isn't always accurate. ↳ Medical advice from forums sound professional. ↳ Investment tips from social media appear authoritative. ↳ Legal interpretations from non-lawyers seem credible. 3/ Your Protection Framework: ↳ Always ask for sources and citations and check them. ↳ Request multiple perspectives on complex topics. ↳ Demand validation for critical claims. ↳ Check geographic and cultural context. ↳ Exercise extreme caution with medical, financial, legal, and mental health advice. 4/ The Reality: With AI project implementations, teams using validation protocols catch significantly more AI errors. The difference is measurable. The internet democratized information sharing. AI has further democratized access to that information. Both are powerful. Neither guarantees accuracy. What validation steps do you use when working with AI? Share below. ♻️ Share with someone who needs to understand AI limitations. ➕ Follow me, Ashley Nicholson, for more tech insights.

I've spent countless hours building and evaluating AI systems. This is the 3-part evaluation roadmap I wish I had on day one. Evaluating an LLM system isn't one task. It's about measuring the performance of each component in the pipeline. You don't just test "the AI"; You test the retrieval, the generation, and the overall agentic workflow. 𝗣𝗮𝗿𝘁 𝟭: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗧𝗵𝗲 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲) Your system is only as good as the context it retrieves. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: How much of the retrieved context is actually relevant vs. noise? ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗲𝗰𝗮𝗹𝗹: Did you retrieve all the necessary information to answer the query? ↳ 𝗡𝗗𝗖𝗚: How high up in the retrieved list are the most relevant documents? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: RAGAs Framework (Repo) https://lnkd.in/gAPdCRzh ↳ 𝗣𝗮𝗽𝗲𝗿: RAGAs Paper https://lnkd.in/gUKVe4ac 𝗣𝗮𝗿𝘁 𝟮: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗟𝗟𝗠'𝘀 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) Once you have the context, how good is the model's actual output? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗙𝗮𝗶𝘁𝗵𝗳𝘂𝗹𝗻𝗲𝘀𝘀: Does the answer stay grounded in the provided context, or does it start to hallucinate? ↳ 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Is the answer directly addressing the user's original prompt? ↳ 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴: Did the model adhere to the output format you requested? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲: LLM-as-Judge Paper https://lnkd.in/gyhaU5CC ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: OpenAI Evals & LangChain Evals https://lnkd.in/g9rjmfGS https://lnkd.in/gmJt7ZBa 𝗣𝗮𝗿𝘁 𝟯: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 (𝗧𝗵𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺) Does the system actually accomplish the task from start to finish? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲: Did the agent successfully achieve its final goal? This is your north star. ↳ 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Did it call the correct tools with the correct arguments? ↳ 𝗖𝗼𝘀𝘁/𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗽𝗲𝗿 𝗧𝗮𝘀𝗸: How many tokens and how much time did it take to complete the task? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞 𝗗𝗼𝗰𝘀: https://lnkd.in/g2TpCWsq ↳ 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴(.)𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗘𝘃𝗮𝗹 𝗖𝗼𝘂𝗿𝘀𝗲: https://lnkd.in/gcY8WyjV Stop testing your AI like a monolith. Start evaluating the components like a systems engineer. That's how you build systems that you can actually trust. Save this roadmap. What's the hardest part of your current eval pipeline? ♻️ Repost this to help your network build better systems. ➕ Follow Shivani Virdi for more.