Prateek Kataria’s Post

📘 Learning Note | 03-10-2025 Yesterday, I explored and deep-dived into LLM Evaluations using OpenAI’s Eval Framework — understanding how product teams assess and monitor the real-world performance of large language models. 🔑 Key Learnings: Types of Evaluations: Unit tests (rule-based), Human vs Model, Model vs Model (A/B), and Reference-based vs Reference-free approaches. Key Metrics: Accuracy, Precision, Recall, F1 Score — crucial for balancing correctness and reliability. Text Similarity Metrics: BLEU, ROUGE, METEOR, and Cosine Similarity — for comparing meaning, not just words. False Positives & Negatives: False positives can be more damaging to user trust, especially in finance or support domains. Practical Demo: Explored OpenAI’s Eval platform — setting similarity thresholds, analysing failed cases, and running automated LLM evaluations. Industry Applications: Evaluations vary by context — exact match for finance, semantic match for chatbots, precision@K for recommendations. Tooling: Learned about SDK integrations, telemetry tools (like Evidently AI), and enterprise evaluation pipelines for continuous monitoring. ✨ Takeaway: LLM evaluations go beyond metrics — they ensure trust, accuracy, and alignment between AI systems and real-world user needs. As PMs, mastering evaluation workflows is key to scaling AI responsibly. #AI #ProductManagement #LLM #OpenAI #AIevaluation #Learning #masai #MasaiVerse #IITPatna #dailylearning #AgenticAI

Small models are quietly making waves and for good reason. When fine-tuned for specific tasks, they can outperform even giants like OpenAI and Gemini. But here’s the catch: their brilliance is contextual, not universal. Unfortunately, much of the media misses this nuance. The headlines shout “Small model beats GPT!” without mentioning it’s only in a narrow domain. This kind of oversimplified reporting doesn’t just mislead the public; it also creates confusion within the developer community. We need more informed conversations, not hype. Let’s celebrate progress accurately and recognise that AI excellence depends on context, data, and purpose, not just model size. #AI #SmallModels #Efficiency #EdgeComputing #AIResearch #ML #MLOps #SustainableAI

Have you seen OpenAI’s newly launched model, Sora 2? It’s insane! 🤯 Of course, like any new and advanced technology, it comes with its challenges (that’s a whole topic for another day). One thing is clear: we need to adapt—fast. Technology is evolving at a really fast pace that it would be unbelievable what would be accessible in the next 5 years. But here’s the truth: AI can only give back what it receives. It can remix, analyze, and generate but it can’t originate like the human mind. What makes us powerful isn’t just efficiency. It’s creativity. 💡 The spark of a new idea that’s never existed before 🌍 The ability to connect dots across culture, emotion, and context 🤝 The intuition to understand people beyond data points AI clears the noise. We create the music. The real opportunity is using AI as a partner. This helps in freeing us to spend more energy on imagination, innovation, and building futures that don’t exist yet. ✨ Embrace creativity. That’s the edge no algorithm can take away. Are you ready to co-create? NB: Everything you see and hear is AI generated! #AI #OpenAI #Sora2 #Creativity #Technology #ArtificialIntelligence

Ok From 4 Hours to 5 Minutes In the world of AI, rapid execution starts not with scale, but with a highly defined problem. As VPE of my Toastmasters club, I encountered a challenge, significant time was spent to manually generate program sheet which could be error prone. I did an experimentation to turn my challenge into a concrete idea: automating club member registration for chapter meetings to allow me to use use prompts to control the dynamic logic of the agenda, speakers, segments, etc to generate a dynamic program sheet. Traditionally, this manual task took me about 4 hours. With the AI agent done in python powered by OpenAI, automation now takes 5 minutes. ⏱️ 98% time savings ⚡ ~48× faster turnaround I echo Andrew Ng’s view, strong AI projects begin with a concrete idea. Concrete idea gives speed, clear direction that can be executed and validated quickly. His insights on AI Product Management: https://lnkd.in/gq49xWUn #AI #AIProductManagement #OpenAI

𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝗡𝗲𝘄 𝗠𝗼𝗱𝗲𝗹: 𝗜𝘀 𝗧𝗵𝗶𝘀 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗟𝗲𝗮𝗽 𝗶𝗻 𝗔𝗜 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Hey everyone! Did you catch the latest buzz around OpenAI? They've been quietly working on something big, and while details are still under wraps, whispers are growing louder about a potential new model that could significantly advance AI capabilities. While we don't have concrete specs (yet!), the speculation is swirling around potential improvements in reasoning, complex problem-solving, and even a more nuanced understanding of context. This could mean a huge leap forward for applications like personalized education, advanced research, and more intuitive AI assistants. Imagine the possibilities! We've seen OpenAI consistently push the boundaries of what's possible, and this upcoming release has everyone in the AI community on the edge of their seats. Of course, with great power comes great responsibility, and ethical considerations will be paramount as these technologies evolve. What are your thoughts on this rumored breakthrough? What applications are you most excited to see impacted by a more advanced AI model? Let's discuss in the comments! 👇 #OpenAI #ArtificialIntelligence #AI #MachineLearning #Innovation #Technology #AINews #FutureofAI #DeepLearning #TechNews Read Full Article Here: https://lnkd.in/gmcbScn5

𝐌𝐋/𝐋𝐋𝐌𝐎𝐩𝐬: 𝐓𝐡𝐞 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐀𝐈 Being in the field of AI, I’ve learned that building a good model is just the beginning. The real challenge and honestly, the most interesting part is making sure that model stays reliable once it’s out in the world. That’s what ML/LLMOps is really about: turning experimental notebooks into systems that can learn, adapt, and stay trustworthy over time. Lately, I’ve been thinking a lot about how LLMOps is shifting from monitoring accuracy to monitoring behavior. Things like prompt drift, retrieval quality, and how user intent changes over time. 💭 𝐎𝐧𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐈 𝐤𝐞𝐞𝐩 𝐜𝐨𝐦𝐢𝐧𝐠 𝐛𝐚𝐜𝐤 𝐭𝐨: How do we build LLMOps pipelines that can self-correct, catching hallucinations or context loss before users even notice? This intersection of RAG, observability, and continuous learning aligns deeply with my career focus — designing AI systems that are not only intelligent but operationally resilient. Curious to hear from others in this space: 👉 What’s one practice or tool that’s made your ML/LLMOps workflow more reliable or less painful? #AI #MLOps #LLMOps #MachineLearning #DataScience #AIEngineering #RAG #PromptEngineering

Is GenAI the only big thing in the industry right now? Is it really the one-stop solution for every problem? Honestly, I was a bit surprised to see people using OpenAI GPT APIs for tasks as simple as parsing fixed-format log files or analyzing structured application performance data. Let's take a step back—can GenAI or machine learning help in these cases? Absolutely!! But it is not without trade-offs such as cost, performance, and effort all come into play. AI is not free, it consumes system resources and adds complexity. GenAI is powerful, but it is not a silver bullet. Use it where creativity and flexibility matter. For deterministic, structured, or performance-critical tasks, traditional approaches still win. #bert #agent #ai #transformer #models #learning #Models#MCP#A2A

Anthropic is seriously stepping up its game, focusing hard on the corporate AI scene and starting to close the gap with OpenAI. They’re not just playing catch-up—they’re tailoring their models specifically for enterprise needs, which is a smart move given how hungry businesses are for reliable, secure AI solutions. Technically, Anthropic’s approach leans on safer, more controllable large language models that emphasize alignment and interpretability—basically, making AI responses less unpredictable and more business-friendly. This means companies can integrate these models with greater confidence, knowing the outputs won’t just be powerful but also trustworthy and easier to audit. For businesses and dev teams, this shift means more tailored AI tools that fit corporate workflows without the usual risks of AI going off-script. If Anthropic keeps this momentum, we might see a real shake-up in enterprise AI adoption, pushing OpenAI to double down on safety and customization. What do you think—will Anthropic’s safety-first angle win more corporate hearts? 🔗 https://lnkd.in/dRCwF5ND #Anthropic #EnterpriseAI #AIAlignment #CorporateAI #OpenAI

AI cracks the toughest financial certification in minutes while humans need years to prepare. Researchers at NYU and GoodFin have revealed that leading AI models from OpenAI, Google, and Anthropic can now pass all three levels of the CFA exam, one of the most challenging certifications in finance. The CFA demands deep reasoning, applied knowledge, and real-world problem-solving, often requiring over 1,000 hours of preparation. Yet, models like OpenAI’s o4-mini scored 79.1% on Level III’s long-form essays, once considered too complex for AI. Gemini 2.5 Pro and Claude 4 Opus were close behind, completing the entire exam in minutes, proving how rapidly AI’s reasoning capabilities are evolving. Stay tuned with Scalebuild AI for more such content. . . . #ArtificialIntelligence #FinanceAI #CFAExam #FintechInnovation #FutureOfWork

Ever wondered what “Machine Learning” or “LLMs” really mean — without the jargon? 🤔 Here’s an easy way to think about it: Machine Learning (ML) is basically how computers learn from experience. Instead of being told what to do step by step, they figure out patterns in data. Example: You feed a model thousands of pictures of cats and dogs. It learns the patterns (ears, fur, size, etc.). Next time you show a new image, it predicts if it’s a cat or dog — even though it hasn’t seen it before. Over time, the model gets smarter as it’s trained with more data. Large Language Models (LLMs) — like GPT or Gemini — are a special type of ML. They’re trained on massive amounts of text from books, code, and the internet to understand and use language. Examples: OpenAI’s GPT Google’s Gemini Anthropic’s Claude LLMs can: Answer questions Write code or essays Summarize documents Chat naturally with humans If ML is the engine, LLMs are the turbo upgrade — built to understand and create language. Together, they’re powering the next wave of innovation — from smart agents to more intelligent business tools. So here’s something to think about: 👉 Will LLMs become our next coworkers… or our creative partners? #AI #MachineLearning #LLM #Innovation #ArtificialIntelligence