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Agentic SLM Sandbox is a small-model-first testbed for building and evaluating agentic systems. It includes minimal controllers for LM-first and controller-first orchestration, tool-calling scaffolds, a secure logger to collect agent traces, and a repeatable LLM→SLM migration workflow (task clustering, model selection, PEFT fine-tuning, routing, and iterative evals). Inspired by “Small Language Models are the Future of Agentic AI” ([ink in comments] and its LLM-to-SLM agent conversion algorithm. Read the paper [link in comments] for more details.

✨ Small Language Models as the Future of Agentic AI The video provides a strong position statement arguing that Small Language Models (SLMs) are the future of agentic AI, despite the current dominance of Large Language Models (LLMs). The authors contend that SLMs are sufficiently powerful, more economical, and operationally more suitable for the specialized and repetitive tasks common in AI agents. They provide arguments grounded in modern SLM capabilities and inference efficiency, advocating for a shift to SLM-first architectures or heterogeneous systems that use LLMs only when necessary. Furthermore, the paper outlines a conversion algorithm to help developers migrate existing LLM-based agents to more efficient SLM solutions and discusses barriers to adoption such as industry inertia and infrastructure investment https://lnkd.in/gUVb8mA7

Large Language Models (LLMs) have rightly captured the spotlight for their broad capabilities and near-human performance across tasks. But as we step into the era of agentic AI—where systems focus on executing a small set of specialized tasks repeatedly—the question arises: do we always need the LLMs? A new perspective argues no. In fact, Small Language Models (SLMs) may be: ✅ Sufficiently powerful for specialized agent workflows ✅ More economical to deploy at scale ✅ Naturally better suited for repetitive, narrow tasks For scenarios that demand broader conversational intelligence, the answer may lie in heterogeneous agentic systems—where multiple models (big and small) work together seamlessly. This vision not only makes agentic AI more efficient but also redefines how we think about scaling intelligence: sometimes, smaller is smarter. Read the paper here: https://lnkd.in/ghK5PfeR

Types of Large Language Models (LLMs) Currently, there are multiple types of LLMs available in the AI space, each with different use cases.

Why Retrieval-Augmented Generation (RAG) is a Game-Changer in AI Large Language Models (LLMs) are powerful, but they come with limitations- most notably, the knowledge cut-off and the tendency to “hallucinate.” That’s where RAG (Retrieval-Augmented Generation) comes in. Instead of relying only on pre-trained knowledge, RAG combines the strengths of: >Retrieval -Pulling facts from external, domain-specific knowledge bases. >Generation -Using LLMs to craft human-like, contextual responses. This means: -More accurate and up-to-date answers -Less hallucination -Custom AI assistants that actually understand your data From legal document review to healthcare insights, RAG is reshaping how enterprises unlock value from unstructured information. >My takeaway: RAG bridges the gap between general intelligence and domain expertise - bringing us closer to truly reliable AI systems. >Curious to hear -where do you see the biggest potential for RAG in the next 2–3 years? #RAG #GenerativeAI #LLM #ArtificialIntelligence #MachineLearning #FutureOfAI

In Agentic AI and the key design patterns, it’s essential to understand how each pattern empowers large language models (LLMs) like GPT to behave more autonomously and effectively. These design patterns push the boundaries of what AI can do by encouraging self-evaluation, tool integration, strategic thinking, and collaboration. Let’s explore four vital agentic design patterns that shape how these models operate and perform complex tasks. Moreover, we are offering a Free Certification Course on Agentic AI Design Patterns: https://lnkd.in/gB4Kf4vE The instructor for this course is Miguel Otero Pedrido #AnalyticsVidya #GIFs #Agentic #Design

The New AI Trinity How RAG, Agents, and MCP Are Building the Future of Intelligent Systems Introduction: The Dawn of the Agentic Era The landscape of artificial intelligence is rapidly evolving beyond the static, reactive capabilities of traditional large language models (LLMs). We are entering a new paradigm defined by a unified and powerful AI stack, where systems are no longer just predictive but are proactive, autonomous, and capable of complex, multi-step reasoning. This fundamental shift is being driven by the strategic convergence of three foundational technologies: Retrieval-Augmented Generation (RAG), AI Agents, and the Model Context Protocol (MCP). RAG Fundamentals: Bridging Static Knowledge with Dynamic Context At its core, Retrieval-Augmented Generation (RAG) is a powerful architectural pattern designed to enhance the output of a large language model by enabling it to reference an external authoritative knowledge base beyond its original training data. It’s not a model in itself, https://lnkd.in/gcDRspYh

Managing interoperability and scalability remains a key challenge for the API community, but a bigger issue is emerging. Siddhant Agarwal points out that "balancing rapid innovation with maintainable and reliable systems is an ongoing struggle for organizations of all sizes." Large Language Models have transformed information access, but they often hallucinate facts and struggle with reasoning across disparate data sources. APIs play a crucial role in connecting structured and unstructured data sources in real time for AI systems. GraphRAG bridges this gap by combining retrieval-augmented generation with knowledge graphs. Read Siddhant's insights on building factual AI systems with APIs in his #apidaysIndia interview: https://lnkd.in/g_Dsgh_h #GenAI #GraphRAG #AIinnovation #AIhallucination

In a world obsessed with using AI to boost productivity, we're often using expensive Large Language Models (LLMs) when a more efficient Small Language Model (SLM) would suffice. What's the general consensus on this?

If you are interested in AI you may see different modern technical jargon related to building and deploying systems that use Artificial Intelligence, particularly Large Language Models (LLMs). Here is a breakdown of each term in the context of AI infrastructure: