SLMs vs LLMs: Cornell paper and Couchbase's Mohan Varthakavi

Generative AI is evolving along two distinct tracks: on one side, savvy users are building their own retrieval-augmented generation (RAG) pipelines, personal agents, or even small language models (SLMs) tailored to their contexts and data. On the other, the majority are content with “LLMs out of the box”: Open a page, type a query, copy the output, paste it elsewhere. That divide — between builders and consumers — is shaping not only how AI is used but also whether it delivers value at all. https://lnkd.in/d4mtMmu6 #2Kategories #AIUse #WhyDoTheyMatter

This article outlines how we can create AI agents that go beyond just generating text, enabling them to perform actions like updating CRMs. I found it interesting that we're shifting our focus from asking AI to write for us to asking it to do tasks on our behalf. How do you envision integrating AI agents into your workflows?

As AI usage grows, the risk of losing structure when transferring outputs is real. Use JSON or Markdown to preserve headings, lists, and context. Matching format to your workflow matters — human editors and machines have different needs. Read more: https://lnkd.in/g8fX4RGZ

As AI usage grows, the risk of losing structure when transferring outputs is real. Use JSON or Markdown to preserve headings, lists, and context. Matching format to your workflow matters — human editors and machines have different needs. Read more: https://lnkd.in/g8fX4RGZ

AI accuracy is critical, especially in high-stakes industries. In my latest byline for Datacentre Dynamics, I explore why smaller, task-specific language models (SLMs) are a smarter choice than large models—offering better accuracy, faster inference, and improved compliance. The future of AI is about the right-sized model for the job.   Read the full article below. I'd love to know your thoughts - feel free to drop me a comment below! #LetsGetToWork #AI #LLM #MachineLearning #SLM #Innovation #AICompliance #Unisys https://lnkd.in/eDeRfsJW

This article explores how AI agents can perform actions beyond just text generation, revolutionizing our interaction with technology. I found it interesting that we can now automate tasks like updating CRMs and managing spreadsheets with AI. How do you think AI agents will reshape your daily workflow?

The article explores how AI agents can now perform actionable tasks, moving beyond mere text generation. I found it interesting that we are shifting our perspective from 'Can AI write this for me?' to 'Can AI do this for me?' This change could revolutionize how we approach workflows and automation. What tasks do you think AI could manage in your daily operations?

Unlocking AI Efficiency: Google’s ReasoningBank Framework for Self-Evolving LLM Agents #ReasoningBank #AIFramework #MachineLearning #LargeLanguageModels #AIEfficiency #AI #itinai #TechTrends #FutureOfWork https://lnkd.in/dZb_nt7J Understanding the target audience for Google’s ReasoningBank framework is crucial for harnessing its full potential. This framework primarily caters to AI researchers, business leaders, and software engineers who are deeply invested in enhancing the capabilities of Large Language Model (LLM) agents. These professionals are typically involved in AI development, product management, and data science, aiming to implement effective AI solutions in enterprise environments. Pain Points Despite the advancements in AI, practitioners face several challenges: Many struggle to effectively accumulate and reuse experiences from LLM agents’ interactions. Traditional memory systems often store raw logs or rigid workflows, proving ineffective in dynamic settings. Failed attempts to leverage these failures into actionable insights hinder progress in refining AI systems. Goals The primary objectives for users of ReasoningBank include: Improving the effectiveness and efficiency of AI agents, especially in completing multi-step tasks. Implementing adaptable memory systems across various tasks and domains. Enhancing decision-making capabilities by integrating learned experiences into AI workflows. Interests This audience is particularly interested in: Cutting-edge advancements in AI technology and machine learning frameworks. Strategies for optimizing AI performance in real-world applications. Research and development focused on memory systems to enhance agent learning. Communication Preferences When it comes to how they like to receive information, the audience typically prefers: Technical documentation and peer-reviewed research findings that delve into the intricacies of AI. Practical applications and real-world case studies that demonstrate the effectiveness of AI frameworks. Clear, concise insights that can be easily interpreted and applied. Overview of ReasoningBank Google Research’s ReasoningBank is an innovative memory framework that enables LLM agents to learn from their interactions—both successes and failures—without the need for retraining. It transforms interaction traces into reusable, high-level reasoning strategies, promoting self-evolution in AI agents. Addressing the Problem LLM agents frequently face challenges with multi-step tasks, such as web browsing and software debugging, primarily due to their ineffective use of past experiences. Traditional memory systems often preserve only raw logs or fixed workflows. ReasoningBank redefines memory by creating compact, human-readable strategy items, enhancing the transferability of knowledge across different tasks and domains. How ReasoningBank...

Make a RAG system they said. It'll be fun they said. Lol. Well, fun or not, it is nice when it works as designed. Fixed some gnarly bugs today. agidreams.us is up to 125 episodes. Very specialized content covered in episodes, and the RAG, if you're curious about AI topics. Chatbot is accessible for free to all by clicking/tapping the chat icon. Can field questions directly from the episode content, or the source content the episode summarized. -- The hierarchical RAG system uses a 4-level semantic chunking architecture (Document→Section→Paragraph→Sentence) to provide adaptive retrieval at different granularities based on query complexity. It stores AGI Dreams episode transcripts in Qdrant vector database with 1024-dimensional Voyage AI embeddings, maintaining parent-child relationships for context expansion. The retrieval pipeline combines vector search, BM25 keyword matching, temporal boosting for recent content, MMR diversity filtering, and BGE reranking to deliver 5-7 contextually-rich results with section anchor URLs. Query complexity analysis automatically selects appropriate chunk levels: simple queries retrieve precise sentences, moderate queries get paragraphs, and complex queries access comprehensive sections with expanded parent-child context. -- Q: tell me everything you know about the goalie mcp. What is it good for? A: Goalie MCP is an agentic research tool built on the Perplexity Search SDK that establishes a new baseline for reliable knowledge work. It breaks down research tasks using goal-oriented planning with A* search algorithms, ensuring systematic approach to complex queries. What makes Goalie particularly valuable is its commitment to verifiability and trustworthiness. The tool ties every model claim to a source citation, allowing users to verify information directly. It also flags conflicting information when encountered, helping users identify contradictions in research materials. Additionally, Goalie includes cryptographic signature frameworks for provenance verification, which enables cryptographic certainty in auditing AI outputs. Goalie operates within the Model Context Protocol (MCP) ecosystem, which functions as a universal adapter allowing language models to connect to external data and tools without requiring bespoke integrations for each provider. This standardization enables Goalie to discover and use thousands of abilities with minimal setup, effectively blurring the line between simple chatbots and fully agentic assistants. As misinformation risks continue to increase with each new model release, frameworks like Goalie point toward a future where AI outputs can be thoroughly audited and trusted with cryptographic certainty, addressing one of the key challenges in the current AI landscape. Sources: linkedin.com (https://lnkd.in/giHjDVWH) MoE Models and Local Inference Tradeoffs (2025-09-29) (https://lnkd.in/gkZ8-WFg)

The divide between builders and consumers of AI is shaping not only how the technology is used, but whether it delivers value at all. http://f-st.co/rryDMD0