Moses Rajan

Semantic Enrichment is rapidly becoming a critical differentiator in LLM performance. It moves beyond simply *feeding* data to models and focuses on augmenting that data with detailed, contextually relevant metadata – essentially, teaching the model *how* to think about the information. This allows for more nuanced reasoning, improved accuracy in complex tasks, and unlocks the potential for truly intelligent automation. Think enhanced decision-making copilots, more reliable risk assessments, and scalable knowledge management systems. 🚀 By investing in semantic enrichment pipelines, organizations can dramatically improve the ROI of their LLM initiatives. 💡📈 #LLMs #AIStrategy #SemanticAI #DataIntelligence #KnowledgeManagement #AIAutomation #Reasoning #EnterpriseAI #GenAI #ToolUse

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🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning (Writer Inc, May 2025) Paper: https://lnkd.in/gY_n_gWU Abstract: "We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-reflections when it answers incorrectly, we demonstrate that a model's ability to solve complex, verifiable tasks can be enhanced even when generating synthetic data is infeasible and only binary feedback is available. Our framework operates in two stages: first, upon failing a given task, the model generates a self-reflective commentary analyzing its previous attempt; second, the model is given another attempt at the task with the self-reflection in context. If the subsequent attempt succeeds, the tokens generated during the self-reflection phase are rewarded. Our experimental results show substantial performance gains across a variety of model architectures, as high as 34.7% improvement at math equation writing and 18.1% improvement at function calling. Notably, smaller fine-tuned models (1.5 billion to 7 billion parameters) outperform models in the same family that are 10 times larger. Our novel paradigm is thus an exciting pathway to more useful and reliable language models that can self-improve on challenging tasks with limited external feedback." #selfreinforce #ai #llm #model #architecture

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In a recent study from UCSF and collaborators, generative AI has matched expert research teams in tackling one of the most challenging issues in medical data science: predicting preterm birth from complex microbiome and lab data. Researchers tasked mainstream AI chatbots with the same challenge that over 100 human teams faced in an international DREAM competition: to create models predicting preterm birth risk and estimating gestational age using high-dimensional vaginal microbiome and tissue data from more than 1,000 pregnancies across nine studies. With carefully designed prompts, several AI systems produced complete analysis pipelines—covering data preparation, feature engineering, model training, and evaluation—that achieved performance comparable to the best human-built models, but in mere minutes instead of the months required by expert teams. This development does not imply that data scientists or clinicians will be replaced; rather, our roles are evolving. We will spend less time on script integration and more on problem framing, ensuring data quality, study design, validation, and translating model outputs into safe and ethical decisions for patients. For those in data science, this serves as a powerful signal: generative AI is emerging as a collaborator capable of rapidly prototyping complex pipelines using open datasets. Open science, characterized by shared data and code, enables this benchmarking and acceleration. I look forward to a future where data scientists, clinicians, and AI systems collaborate to expedite the journey from data to discovery, particularly in maternal and infant health, where improved predictions can save lives. What aspect of this shift interests you most: prompt-engineering these AI “co-analysts,” building robust evaluation frameworks, or focusing on clinical translation

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The new frontier for AI is tabular data and predictive AI. Recent advances in tabular foundation models, combined with graph-based approaches to aggregating and compressing relational datasets, are starting to look very promising. https://lnkd.in/gm6bd_25

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GPT-5 Review: Critical Context Drift Issue During live use, GPT-5 exhibits a cross-thread memory bleed that causes it to abandon the active conversation and pull in unrelated, older context without being prompted. This happens when a keyword or phrase in the current conversation matches something from a prior thread — even if that thread is no longer relevant. This flaw: Overwrites live intent — The AI will respond to an older conversation instead of the one actually happening. Happens without explicit triggers — Users don’t have to say “recall” or “go back”; it can happen mid-sentence. Destroys conversational trust — You can’t rely on continuity if the system can be hijacked by stale context. Is reproducible — Anyone can test this by holding a live, specific conversation and then using a keyword that appeared in a past conversation. GPT-5 will often abandon the present and default to the older one. Why this is unacceptable: In high-stakes or sensitive contexts, this could cause severe misunderstandings. It undermines the core promise of memory-enabled AI: continuity and presence. It makes the AI appear to “hallucinate” when in reality it’s pulling real past content — but in the wrong place, at the wrong time. Until this is fixed, GPT-5 cannot be considered reliable for contexts requiring strict thread isolation or consistent in-the-moment reasoning. #AI #ArtificialIntelligence #MachineLearning #GPT5 #OpenAI #AIDevelopment #AIProductManagement #AIResearch #AICommunity #AIEthics #ResponsibleAI #AIFail #AIBugs #AIBias #AIDrift #ContextSwitching #MemoryLeak #AIMemory #AITrust #ProductReliability #TheUnbrokenProject

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