Stanford & SambaNova research challenges "brevity bias" in LLMs, proposes ACE for dynamic prompts.

🧠 **AI That Actually "Thinks" Before Answering** The latest AI models like Claude 3.7 Sonnet and OpenAI's o1 now use thinking tokens - giving them time to reason through problems before responding. How It Works? Instead of rushing to an answer, the AI: - Works through problems step-by-step (serial reasoning) - Explores multiple solutions simultaneously (parallel reasoning) - Stops when it finds the answer (no wasted tokens) The Results Claude 3.7 Sonnet with extended thinking: 84.8% on GPQA (challenging science questions), including a 96.5% physics score. The best part? You can watch the AI think in real-time - seeing it explore ideas, catch mistakes, and refine answers. The Trade-Off More thinking = Better accuracy, but slower responses and higher costs. Developers now control: ✓ Thinking budgets based on task complexity ✓ Speed vs. quality balance ✓ Transparent reasoning without fine-tuning Bottom Line We're moving from AI that "blurts out" answers to systems that genuinely reason. The future is hybrid models that know when to think fast and when to think deep. Just like humans. #AI #MachineLearning #ArtificialIntelligence #TechInnovation #LLM

🧠 RAG isn’t one-size-fits-all — it’s evolving fast. Retrieval-Augmented Generation (RAG) started as a simple idea — retrieve, augment, generate. But now, it has multiple forms that make AI smarter, more factual, and more adaptive. Think of it as different “modes” your AI can switch between 👇 Here’s what you’ll learn in this post: ✅ Standard RAG → the base flow — retrieve + generate ✅ Interactive RAG → human in the loop — refine answers live ✅ Fusion RAG → combines multiple retrievers for richer recall ✅ Agentic RAG → agents plan, query, and reason autonomously ✅ Multi-Pass RAG → iterative retrieval for higher accuracy 💡 Key Takeaway: Each RAG type solves a unique problem — together, they form the backbone of modern AI copilots, researchers, and assistants. 🔗 Explore all RAG types here: 👉  ✍ Content created by Niket Girdhar 📌 Missed the earlier parts? Catch up here: 🔗 Day 1 (RAG Foundation): https://lnkd.in/dEKT8d99 👥 Follow Peer Grid for simple, visual explainers on AI, tech, and systems. #PeerGrid #RAG #RetrievalAugmentedGeneration #AI #LLM #MachineLearning #GenAI #Embeddings #TechExplained

What if AI could learn and adapt without ever retraining its core model? Could the future of intelligence lie not in changing weights—but in evolving context? How might “living prompts” reshape the way we interact with AI systems? RIP fine-tuning ☠️ A groundbreaking new Stanford paper just dropped—and it’s a game-changer. The paper on Agentic Context Engineering (ACE), demonstrates that you can dramatically boost model performance without modifying a single weight. Instead of fine-tuning, ACE evolves the context itself. The model writes, reflects on, and iteratively edits its own prompt—turning each interaction into a step toward self-improvement. Imagine the model maintaining a dynamic, ever-growing notebook: - Every failure becomes a new strategy. - Every success becomes a reusable rule. The results? Nothing short of remarkable: - +10.6% over GPT-4–powered agents on AppWorld - +8.6% improvement in finance reasoning - 86.9% lower cost and latency - All achieved with zero labels—just feedback For years, we’ve chased “short, clean” prompts. ACE flips the script: it builds long, rich, evolving playbooks that accumulate knowledge over time. Why? Because LLMs don’t crave simplicity—they thrive on context density. If this approach scales, the next wave of AI won’t be fine-tuned… It’ll be self-tuned. Welcome to the era of "living prompts". #AI #LLM #PromptEngineering #ACE #SelfImprovingAI #ContextOverWeights #FutureOfAI #StanfordResearch

🎓 AI Engineering Daily — Day 7: RAG Gives AI Memory. Agents Give It Autonomy. Every foundation model has one big flaw: it doesn’t know what it doesn’t know. That’s where RAG comes in, Retrieval-Augmented Generation. Instead of forcing the model to memorize everything, RAG teaches it to look things up before answering. It works in two steps 👇 🧩 Retriever: finds relevant information. 💬 Generator: uses that information to craft the response. Retrievers come in two main types: 1. Term-based: matches exact words (like search engines). 2. Embedding-based: finds meaning (semantic similarity). The best systems combine both; hybrid retrieval. Then you fine-tune the pipeline with chunking, reranking and query rewriting, so the model gets only what it needs, when it needs it. But the next leap is even bigger: Agents. Agents don’t just answer, they act. They can browse, call APIs, execute code, and plan multi-step workflows. RAG gives AI memory. Agents give it autonomy. Together, they turn static models into interactive problem-solvers. 📘 This is Day 7 of AI Engineering Daily: Decoding the Future of Building Applications with Foundation Models. #AIEngineering #RAG #Agents #LLMs #FoundationModels #GenerativeAI #TechLeadership #AIEngineeringDaily

🧠 Every time an AI struggles to recall past context, it reminds us - we built intelligence without memory. LLMs today can process vast windows of data - up to 128k tokens - but they don’t yet retain knowledge across interactions. Recent breakthroughs like 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 𝗠𝗲𝗺𝗼𝗿𝘆 𝗕𝗮𝗻𝗸 and the 𝗠𝗲𝗺𝗢𝗦 framework aim to fix this. They’re building persistent, hierarchical memory systems that decide what to retain, forget, and recall - moving AI from reactive models to relational systems that truly understand context. At Curo,we’re designing learning systems with structured memory - not static recommendation loops. Our AI companion builds a persistent map of your learning journey - understanding context across sessions, retaining past insights, and resurfacing the right ones when you need them most. Because real learning - human or AI - isn’t about knowing more. It’s about remembering what matters. 🌐 www.curohq.com #AI #AIMemory #ContextEngineering #BuildInPublic #Curo

🚫 The Goldilocks Problem of AI: Overfitting vs. Underfitting! 🧠📉 Every Machine Learning model has one critical mission: to generalize well to new, unseen data. But often, they fall into two traps���the Goldilocks problem of AI! 🐻🥣 Overfitting (Too Hot!): The model memorizes the training data perfectly, making it useless in the real world. 📉 Underfitting (Too Cold!): The model is too simple and misses the core patterns entirely. ❄️ The goal is to find the "Just Right" balance—a model that captures the signal without memorizing the noise! 🎯 Understanding this balance is non-negotiable for building reliable, scalable AI. In this carousel, we crack the code on model performance pitfalls: 🎯 The Pitfalls: Defining Overfitting and Underfitting. 🧐 📈 The Visual Test: How to spot the problem on a graph. 📊 ✨ The Solutions: Essential techniques like Regularization and Cross-Validation. 🛠️ 💡 The Goal: Achieving optimal generalization. 🌐 Swipe to learn how to build AI models that truly succeed in the wild! 👇 #Overfitting #Underfitting #ModelValidation #MachineLearning #AIExplained #DataScience #Regularization #TechSkills https://vivekkudecha.dev #AI #ML #cfbr #new

How do models like GPT and Claude really understand language? It’s not magic. It’s the "Attention Mechanism." But the original Attention that powers them has a massive computational bottleneck: O(N^2) quadratic complexity. So, how do today's massive models handle 100k+ token contexts so efficiently? The answer lies in a series of brilliant optimizations. I’ve just published a deep-dive article breaking down the evolution of the engine that powers modern AI. We explore the critical breakthroughs that make today's LLMs feasible: ⚡ Self-Attention: The "Attention Is All You Need" concept. ⚡ Multi-Head Attention (MHA): The parallel power... and the O(N^2) problem. ⚡ Grouped-Query Attention (GQA): The elegant compromise for inference. ⚡ Sliding Window Attention (SWA): The solution for long-context efficiency. ⚡ Flash Attention: The I/O-aware breakthrough that changed the game. These aren't just academic terms; they are the engineering breakthroughs that define the limits of AI performance. If you're building, managing, or just curious about the core mechanics of AI, this is a must-read. Which of these optimizations (GQA, SWA, or Flash Attention) do you believe has had the biggest practical impact on the models you use daily? Let's discuss. #AI #LLM #DeepLearning #Transformers #AttentionMechanism

Benchmarks often don't tell the full story — and that is especially true in the Document AI space. Modern VLM-based parsers are stochastic and can produce multiple valid interpretations of documents, and yet most Document AI vendors still evaluate their models with OCR-era metrics, which were built for a time when every page had one “right” answer. Those legacy metrics penalize valid alternate representations, which can even flip benchmark rankings entirely. At Unstructured, we built SCORE to fix this. SCORE (Structural and Content Robust Evaluation) is our semantic framework for evaluating modern, generative document parsing outputs — and it’s the foundation we use to optimize our own VLM parsing strategy. So if you’re evaluating document AI solutions, ask this first: "How are they measuring quality?" If they’re not using something like SCORE, their metrics aren’t giving a complete, honest picture of performance. Don’t buy the benchmark. Benchmark the buy. #DocumentAI #AIevaluation #Benchmarking #UnstructuredIO #SCORE #AI #GenAI #ETL #ETL+ #RAG #UnstructuredData #LLM #MCP #EnterpriseAI #RAGinProduction #LLMready #Unstructured #TheGenAIDataCompany

If you’re in the software space, you’re probably already thinking about bringing AI into your workflow. From our experience over the past two years exploring AI in the data space, one thing has become clear — getting the exact outcomes you expect is rarely 100%. The models are powerful, but they’re still evolving. Despite that, we’re more confident than ever that as this field matures, we’ll see massive improvements, better tools, and more reliable results. So if you haven’t started your AI journey yet — start now. And if you already have — keep going. The learning curve is steep, but the possibilities are endless. Interestingly, I just read that China’s open-source model Kimi K2 Thinking has reportedly outperformed GPT-5, Claude, and other leading models — a reminder of how fast this space is advancing. #puredata #ai #KimiK2Thinking

The Next Era Isn’t AI. It’s Understanding. We’ve spent years building systems that can answer. Now we’re entering an age where systems will understand. RAG, agents, and generative pipelines aren’t just automating content — they’re creating context bridges between people and information. Soon, knowledge won’t live in PDFs or dashboards. It’ll talk back. It’ll teach you. It’ll adapt to what you already know. That’s not artificial intelligence — it’s amplified understanding. And the teams building it now will define the language of the future. #AI #RAG #FutureOfWork #AITransformation #SynFlowAI #UnderstandingMachines