Mani Smaran Nair’s Post

Lean AI - How to Reduce LLM Cost? Staying ahead in GenAI means not just adapting to change but embracing it. Our journey centres around leveraging LLM to its fullest potential, focusing on two main areas: operational efficiency and customer experience. The integration process involved several key strategies, each tailored to harness the power of LLM with cost optimization in mind. 💰 1. Fine-Tuning: We customized pre-trained Large Language Models (LLMs) with task-specific data, significantly enhancing performance for specialized tasks. 💰 2. Model Cascade: A tiered model approach was adopted, using simpler models for basic tasks and escalating to more complex ones as needed, ensuring efficiency. 💰 3. Model Router: Intelligent routing of queries was achieved by distributing tasks to the most appropriate models based on their complexity. 💰 4. Input Compression: We optimized the preprocessing of input data to reduce size and complexity, saving on computational costs. 💰 5. Memory Summarization: In our chatbots, conversational memory was optimized by summarizing past interactions, and efficiently managing memory load. 💰 6. Model Quantization: We reduced the precision of LLM parameters, which decreased computational demands, allowing for lower costs and faster processing. 💰 7. Agent-Call Cache: A caching system for responses from multi-agent collaborations was implemented to save on repetitive computation costs. 💰 8. Lean Components: Streamlining AI components helped reduce unnecessary token consumption, optimizing the input/output process. The insights offered are particularly relevant for startups and businesses looking to integrate LLM into their operations while maintaining cost-effectiveness. I am [Benny](https://lnkd.in/gN_URA8J) ✔ A AI/ML solution architect, and a long-time AI lover who has gone through the AI winter and revolutions. ✔ I talk about enjoyable AI, computational beauty, and everything in between. ✔ I regularly shared passion research articles at [Benny's Mind Hack](https://lnkd.in/gPGNMx5f) for over 8 years. #GenAI #LLM #CostReduction #LeanAI #Innovation #BusinessStrategy

AI agents, usually powered by an LLM, are systems designed to act autonomously to achieve a specific goal. They receive an input prompt and have access to a toolset needed to complete certain tasks. The input prompt can take several forms. It can be a simple text prompt given by a human with instructions to follow, such as “Write a blog post about AI Agents.” In a Multi-Agent System (MAS), it can be the output of the previous agent, which can also be text or more structured data such as JSON.

𝐈𝐒 𝐀𝐈 𝐀 𝐏𝐀𝐒𝐒𝐈𝐍𝐆 𝐅𝐀𝐍𝐂𝐘? Are AI companies really plateauing, that is, "atrophying" or our perceived notions of progress or more accurately, lack of, true? The average adoption rate of AI globally stands at 26%-35% (regional variance), India is the only country on the planet whose average adoption rate stands at 30%-40%. That means that the greater majority of the world either has never heard of AI or has no idea what it does. 𝐈𝐬 𝐀𝐈 𝐏𝐥𝐚𝐭𝐞𝐚𝐮𝐢𝐧𝐠? What the papers consider as "plateauing", just means that the rate at which features and tools are launched has diminished and is now becoming more standardised. Even o1-Preview is relevant as of September 2021; I don't know if you noticed, but that's more than 3 years ago. And if that is the level of reasoning we see, that also means the Computer Use by Claude isn't really new, it was just launched recently. 𝐀𝐫𝐞 𝐖𝐞 𝐚𝐭 𝐭𝐡𝐞 𝐅𝐨𝐫𝐞𝐟𝐫𝐨𝐧𝐭 𝐨𝐫 𝐣𝐮𝐬𝐭 𝐃𝐨𝐧'𝐭 𝐊𝐧𝐨𝐰 𝐀𝐧𝐲 𝐁𝐞𝐭𝐭𝐞𝐫? Even the concept behind it isn't new: Remotely controlling computers has been the staple of ITES companies since the 1960s, when the original developments started as early as the late 19th century by the likes of Nikola Tesla. The current iteration though, is the innovation. And even had this innovation been considered "old", for the early adopters it isn't. 𝐓𝐡𝐞 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞 𝐚𝐦𝐨𝐧𝐠 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐢𝐬 𝐋𝐨𝐰𝐞𝐫 𝐭𝐡𝐚𝐧 𝐀𝐦𝐨𝐧𝐠 𝐈𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥𝐬 Businesses, for the most part, are not even within the early adopters. Many businesses find resistance either from the executives, who can't yet pinpoint the ROI of using AI, or the employees who are afraid of losing their jobs if their companies do decide to adopt AI tomorrow. It would actually be nice to see launches which are not every 6 weeks, as that creates major confusion for companies which are looking to adopt AI tech for their business and have to rethink their strategy every quarter. #ai #adoption #claude

Is AI progress starting to 'slow down'? Have we 'tapped out' all the data? Short Answer: lol, no 😂 Long Answer: AI's momentum is parallelized now, meaning there are several viable pathways to build on (both on the research and scaling side). Some models underperform expectations, and some models aren't released due to competitive concerns or safety and alignment concerns. You cannot judge progress based on consumer-facing product releases. The best way to track the frontier is through research developments. As Ilya said, "Scaling the right thing matters more now than ever," hence the focus on parallelization. Both OpenAI and Anthropic have said they have a clear line of sight on where to build for the next 18-24 months. Beyond that, it is hard to plan because the frontier is actually moving so quickly (except for things like large infrastructure projects which can have long lead times). Why are people saying this then? These comments are usually taken out of context. They often reflect one small part of the picture and can be misleading. A quick litmus test is to ask if they know what Arxiv is (https://lnkd.in/gZWd7gwY) or what the most recent research paper they read was. If they can't answer, it's unlikely they are tracking the broader AI landscape. "Not much has happened since ChatGPT" — what do you say to this? Some of the biggest developments have occurred in the last two months and came sooner than many in the field expected. Here are a few examples: - Test time training/compute: https://lnkd.in/gSeFqG4b - Real-time voice API: https://lnkd.in/gK8bKeEK - Computer Use: https://lnkd.in/gn_8f222 Each of these has the potential to transform industries. The thing that stumps most people in this industry is how little coverage these developments have gotten. So when someone says AI progress is "losing steam," ask them what research papers they read to form this opinion... 🙃

🚀🔥 Day 3 - 25 Days of AI for ALL in 2025 🔥🚀 🚀 AI Agents: Beyond Just Wrappers Over LLMs 🌟 AI agents aren’t merely wrappers around Large Language Models (LLMs); they’re much more sophisticated and nuanced than that. The LLM, often referred to as the "brain," provides immense potential, but having a brain doesn’t automatically mean it will excel without direction. The real challenge lies in building agentic frameworks that effectively leverage this brain to deliver useful outputs and take meaningful actions. It’s about moving beyond just what the LLM can generate and into how it operates in complex, real-world environments. 🔑 Key problems every agent designer must solve include: 1️⃣ Optimizing Memory: How can agents retain and recall information effectively without bloating performance? 2️⃣ Building Robust Retrieval-Augmented Generation (RAG) Systems: Ensuring agents are informed by relevant, real-time data. 3️⃣ Improving Output Quality and Efficiency: Striking the balance between coherence, relevance, and speed. 4️⃣ Integration Across Diverse Environments: Making agents versatile enough to operate in dynamic, multi-system landscapes. Designing effective agents involves countless decisions about workflows, architecture, integrations, and optimizations. Ironically, the LLM itself might be the least unique part of this process—since the same models are widely accessible. The true innovation lies in how you design, build, and refine agents to solve specific problems, deliver value, and adapt seamlessly to unique contexts. 💡 As the AI landscape evolves, the competitive edge will belong to those who master these nuances. What’s your take on crafting smarter agents? Let’s discuss! 💬 💡 Follow me for daily inspiration, insights, and resources in this 25-day AI series. Together, let’s make 2025 the year of AI for ALL! 🌟Credit: Cygaar. #AI #ArtificialIntelligence #LLM #AIAgents #Innovation #MachineLearning #TechLeadership

Nice, detailed overview of why #AI Agents will have the biggest impact in leveraging AI to solve business problems in the future. It's really not "all" you need, but I've seen first hand how chatbots tend to solve just one part of a process. There is a place for both, but pay attention to Agentic Workflows that leverage your own data outside of LLM's. Whether it's via Actionable Language Models (ALM's) or Small Language models (SLM), more attention should be on these approaches.

As AI continues to advance, multi-agent systems are emerging as a solution to the limitations of traditional models like Large Language Models (LLMs) in handling complex, multi-dimensional problems. For instance, while LLMs excel in tasks like text generation, they struggle with real-time decision-making, long-term planning, and managing tasks that require coordination between multiple agents. Multi-agent AI systems, on the other hand, divide complex problems into specialized tasks, with each agent focusing on a specific aspect. This allows for greater collaboration, quicker responses, and better adaptability to changing environments. Multi-agent systems can also scale more easily, handle ambiguity by offering multiple strategies, and adjust to new contexts in real-time, something LLMs often can’t do without retraining. In dynamic environments like autonomous driving or financial trading, where decisions must be made quickly and collectively, multi-agent systems provide the flexibility and intelligence that LLMs lack. In summary, multi-agent AI is better equipped to tackle challenges requiring real-time collaboration, adaptability, and scalability—capabilities that go beyond the scope of LLMs alone.

A Game-Changer for AI 🚀 RAG: The Future of Intelligent Systems What happens when you combine the brilliance of large language models (LLMs) with the accuracy of real-world data? You get Retrieval-Augmented Generation (RAG)—a technology that's reshaping AI as we know it. 💡 Here’s how it works: RAG enhances traditional AI by pulling data from external knowledge bases during the generation process. It’s like giving AI access to a library of real-time, verified information to ensure accurate and contextually relevant outputs. Why does this matter? Traditional AI models rely on static, pre-trained data, which can become outdated or irrelevant. RAG changes the game by dynamically retrieving updated information, making it ideal for industries that require precise, timely responses. 📣 Want to dive deeper into how RAG works and its real-world applications? Join us for our upcoming webinar on RAG where we’ll explore its transformative potential! 👉 https://lnkd.in/drnpB8JZ 💬 Think about it: How could RAG help your organization make smarter, faster decisions? Share your thoughts below #ArtificialIntelligence #MachineLearning #AIFuture #TechInnovation #DataDriven #RetrievalAugmentedGeneration #AIWithData #IntelligentSystems #RealTimeAI #KnowledgeRetrieval #TechTrends2024 #FutureOfWork #SmartDecisions #DigitalTransformation #InnovationForGood #TechConnect #AutomationTrends #AIApplications #CustomerExperience #ContentCreation #TechWebinar #AIForBusiness #EmergingTechnologies

🧠💬 Data Brew Talk 💻☕ The Power of LLM Agents – Part 1/3 What makes LLM Agents stand out in the world of AI? Their complementary strengths create a system far more powerful than either could achieve alone. Here’s why this matters: 1️⃣ Language Understanding + Actionable Execution LLMs are experts at understanding human language, whether it's nuanced commands or complex queries. But understanding alone isn’t enough. Agents step in to execute those instructions, turning comprehension into real-world actions. Together, they deliver a seamless flow from intent to result—like when you ask a chatbot for help, and it not only understands you but solves your problem. 2️⃣ Flexibility + Structured Precision LLMs excel at handling ambiguous, open-ended inputs, making them ideal for creative problem-solving. But when precision is required, agents shine. They follow detailed workflows, ensuring tasks are completed accurately and systematically. This mix of adaptability and rigor makes LLM Agents effective in diverse tasks, from brainstorming to task automation. The magic lies in how LLMs and agents complement each other, creating a smarter, more responsive system. In Part 2, we’ll dive deeper into how they pair reasoning with action for even greater results! #AI #LLMAgents #AIAgents #LLMs image credits: https://lnkd.in/gJP6Scb8

IndyDevDan is probably the most value packed channel when it comes to testing and using LLM's in our daily workflows. This is another great video to watch if you are trying to navigate your way through the LLM landscape. Key takeaways: 1. IndyDevDan  introduces a framework for categorizing Large Language Model (LLM) use cases to streamline generative AI work. 2. He Introduces Six Categories of LLM Use Cases: • Expansion: Generating content, learning, ideation (e.g., writing a blog post intro). • Compression: Summarizing, extracting key information (e.g., summarizing a product release). • Conversion: Changing formats (e.g., text-to-SQL, language translation). • Seeker: Finding specific information (e.g., finding the best-performing product in a sales report). • Action: Executing commands, tool calling (e.g., generating Git commands). Reasoning: Providing judgments, conclusions, driving decisions (e.g., recommending an authentication method). 3. He Discusses the Benefits of Categorization: • Faster Decision-Making: Categorizing helps in choosing the right tools and prompt structures. • Simplified Prompt Engineering: Each category has specific patterns and tooling needs. • Reusable Benchmarks: Benchmarking methods can be reused within each category. • Guides Agentic Design: The framework helps structure AI agents by chaining prompts from different categories. https://lnkd.in/gJhUqy7A #GenerativeAI #LLMs #PromptEngineering