Enhancing structured outputs with trust scores

One of the major hurdles in adopting LLMs for many companies has been the risk of model-generated hallucinations and a lack of transparency. In response to this, there has been a concerted effort to enhance monitoring mechanisms and establish robust checks and balances around these models. This includes the development of more transparent evaluation models that align closely with human judgment (JudgeLM - https://lnkd.in/e8Xspek9), the implementation of customizable evaluation criteria tailored to specific business needs (FoFo - https://lnkd.in/eAMutjXJ), and open-source frameworks like DeepEval (https://lnkd.in/eYMB-Xiw ) which track aspects such as toxicity and hallucination using a variety of NLP models such as QA bi-encoders, vector similarity tools, and NLI models. This week, a few additional methods and frameworks were intoruduced: 1. Cleanlab's 𝗧𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗧𝗟𝗠) incorporates a trust score for each output, enhancing reliability and transparency by indicating the likelihood of an output being accurate. This framework is particularly helpful for customer facing applications and where the cost of errors is high (https://lnkd.in/e9FUztgj) 2. 𝗣𝗿𝗼𝗺𝗲𝘁𝗵𝗲𝘂𝘀 𝟮 (https://lnkd.in/er_3nqGt) released by LG researchers is developed using weight merging of separately trained evaluators: one that directly scores the outputs (direct assessment) and another that ranks the outputs (pairwise ranking). In extensive benchmark tests across both direct assessment and pairwise ranking, Prometheus 2 achieved the highest correlations and agreement scores with human evaluators, demonstrating a substantial advancement over existing methods. 3. “When to Retrieve” (https://lnkd.in/euANnwWg) presents an innovative model, the 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗟𝗟𝗠 (𝗔𝗗𝗔𝗣𝗧-𝗟𝗟𝗠), which intelligently decides when to utilize external information retrieval to enhance its question-answering capabilities. ADAPT-LLM is trained to generate a special token ⟨RET⟩ when it needs more information to answer a question, signifying a retrieval is necessary. Conversely, it relies on its intrinsic knowledge when confident in its response. This model outperformed fixed strategies like always retrieving information or solely relying on its own memory. There is no doubt that significant investments are being made to enhance the robustness and reliability of LLMs, with stringent checks and balances being established. Meanwhile, tech giants like Google are not shying away from deploying LLMs in sensitive areas, as seen with their MedGemini project - a highly capable multimodal model specialized in medicine, demonstrating superior performance in medical benchmarks and tasks (https://lnkd.in/epbv63iN). These developments indicate a rapid progression towards broader and more impactful deployments of LLMs in critical sectors soon.

Why LLM Governance Must Move From Principle to Practice—And Why AIGN Sets a New Global Standard Last quarter, I worked with a global insurer deploying LLMs for customer-facing workflows. They had compliance checklists—yet when an LLM output was challenged by a regulator, no one could explain who owned the prompt, how the decision was validated, or how hallucination risk was monitored. The result? Delays, rework, and real reputational risk. This isn’t rare—it’s the emerging norm. Here’s the reality: Over 65% of organizations are running LLMs in production. Yet less than 20% have robust governance. ([McKinsey & Company, Accenture 2024]) We all want innovation, but without structure, scale quickly becomes exposure. That’s why frameworks like @AIGN—championed by leaders like Patrick Upmann—are game-changers. Unlike traditional checklists (#ISO42001, #NIST, #OECD), AIGN provides a practical operating system for governance: - LLM-Specific Controls: Real-time prompt registries, hallucination mapping, and versioning—no more black boxes. - Operational Role Models: Clearly defined Prompt Owners, Output Validators, and Ethics Reviewers to end finger-pointing. - Dynamic Risk & Ethics Domains: Actionable risk tiering and approval workflows—not just policy, but day-to-day decision-making. - Trust Scorecards & Trust Label: Make governance visible and measurable—internally and externally. - Continuous Lifecycle Oversight: Review cadences, anomaly detection, and user feedback so governance grows with your LLMs. In my own advisory work, implementing these steps helped one client detect and remediate a bias issue before it hit production—a save that protected both their brand and bottom line. My challenge to AI leaders: - Don’t settle for theoretical compliance. Benchmark your LLM deployments against AIGN’s standards. - Prioritize role clarity and explainability—these are your best risk controls. - Publish your trust metrics—transparency is the new differentiator in AI. Let’s share: What’s the toughest LLM governance gap you’ve seen—risk assessment, explainability, or role confusion? How did you solve it (or what help do you need)? Kudos to Patrick Upmann and the @AIGN team for leading by example. #LLMs #AIGovernance #AITrust #ResponsibleAI #OperationalAI #Explainability #AICompliance #AIGNFramework #AILeadership

Generative AI often produces hallucinations, misleading or inaccurate outputs, reducing reliability and trust. A new approach aims to refine AI-generated content while improving clarity and transparency. 🛠️ 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: A multi-agent system using 300+ prompts to induce, detect, and correct hallucinations. AI agents review outputs through: ✅ Different language models ✅ Structured JSON communication for precise refinement ✅ The OVON framework for seamless AI collaboration ✅ New KPIs to quantify and mitigate hallucination levels 📈 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: 🔹 Reduced hallucination scores 🔹 More transparent speculative content 🔹 Enhanced AI explainability through structured meta-information As AI systems grow, refining their reliability is crucial. Multi-agent approaches might be a key step toward more trustworthy and explainable AI. Link to the research paper: https://lnkd.in/dfegT67V

Post-processing is where the future of enterprise AI is quietly being won or lost. With the explosion of LLM and RAG-powered platforms on Databricks and Snowflake, organizations are shifting from building high-speed models to building trust, quality, and compliance into every output. These platforms are operationalizing validation. Imagine financial transactions or regulatory documents processed by LLMs, before results reach business users, robust post-processing checks kick in: --> On Snowflake, AI agents validate referenced passages in real time, cross-checking generated answers with structured and unstructured data sources. “Needle-in-the-haystack” tests and entity-matching algorithms tag outputs with confidence scores. --> Databricks now embeds post-processing flows inside notebook jobs and MLops pipelines using BLEU, ROUGE, and advanced semantic similarity (via MLflow and vector search) to flag hallucinations and monitor self-consistency across thousands of outputs. These metrics are streamed back for data lineage and audit logs, directly supporting compliance. Both platforms are integrating audit logging and compliance as first-class features. Snowflake’s Trail and Databricks’ Unity Catalog don’t just trace data movement, but they capture every AI operation, scoring, and correction. This closes the feedback loop for responsible AI in financial services, healthcare, and beyond. This matters because, as we move to semantic layers, self-serve analytics, and domain-specific copilots, post-processing is no longer an afterthought, but it’s the trust layer for enterprise AI. Teams can now validate, revise, and log outputs at scale without manual intervention. Enterprise AI must be explainable, auditable, and built for compliance from day one. #HumanWritten #ExpertiseFromAndForfield #LLM #Databricks #Snowflake #AIquality #DataEngineering #Compliance #Auditability #EnterpriseAI