Understanding Leading Large Language Models (LLMs)

1. LLM Introduction: Understanding leading large language models, LLMs. In this lesson, we will learn about the major large language models in the market today. You will understand what each model does well, whether it is struggles and where does it struggle and how to choose the right one based on the use case, budget and compliance needs. 2. What is a LLM?: What is an LLM? It is a digital system that is trained on a huge amount of text. It learns patterns, context and meaning, so it can generate answers, summaries, ideas, and even code. Different companies build different models. Each has its own strength and limitation. There is no single best model. The right choice always depends on what problem you want to solve. 3. OpenAI-GPT Family: OpenAI, it's also called us the GPT family. OpenAI has models like GPT 3.5, four, 4.4 and GPT five. They are good at excellent reasoning and problem solving. They have a strong creativity. GPTs works smoothly with Microsoft tools, great for automation and content creation. Things to keep in mind is the higher cost when used in large scale. It may generate incorrect or made up answers. It is a closed source, which means you have limited control. 4. Anthropic Claude: Anthropic Claude family. Cloud models are designed with a strong focus on safety and responsible outputs. What they are good at reading and summarizing long documents, compliance and legal task, policy reviews and contract understanding. Things to keep in mind can be overly cautious. Smaller ecosystem when compared to OpenAI. Next, we will learn about Google Deep Mind or the Gemini family. 5. Google Gemini: Google Deep Mind or the Gemini family. Gemini models can work with text, images, audios, and videos. They are good at multi model task. Deep integration with Google Workspace, internal knowledge search across documents and emails. The things to be kept in mind is that it has slower release cycles. Limited availability outside the Google tools. Meta or Lama family. 6. Meta LLama: Meta or ama family. Lama models are open source, which means you can modify them, post them privately. Lower cost, especially for private environment or deployment. It is useful for teams with strong technical skills. Things to be kept in mind while deploying ama is that it requires in house expertise, not as easy to set up a closed source option. 7. Mistral AI: Mistral, it's also called Mistral or Mistral seven B. Mistral is a new company known for efficient lightweight models. What are they good at? There fast and efficient reasoning. Good for code generation. Things to be kept in mind that it is still growing in an enterprise support, not yet as matured as the large players available in the market. 8. Cohear Command R family: Chair or command or family. The logo looks a little different, right? Cohare focus on Ag retrieval augmented generation, which means it links AI to real company data. What are they good at? They are good at accurate answers based on enterprise documentation. Internal chatbots for HR, claims, finance, or IT can be a good place to deploy this. Strong for knowledge retrieval task. Things to be kept in mind is that it is not very popular among general users. It is limited at multimodal capabilities. 9. Grok AI: Grok AI or Grog Grok is XAI's model. It is known for real time access to Twitter data. They are good at social media trend tracking. Candid and less filtered response. Early potential for analytics. We have to be careful that it is not ideal for a regulated industry. It is still very new and untested at scale. 10. AI Operating Model-the Three A's: Let us now look at AI, not as a technology, but as three very different operating models for an enterprise. The first is automation. Here, AI eliminates repeatable tasks which are rule based work at scale. For a transformation leader, this isn't about summarizing emails. It is about re engineering the workflows. For example, global insurers use automation to prescreen claim instantly, ensuring compliance while cutting millions in processing cost. The leadership question is, where do we free up the capacity and redeploy talent for a higher value activities? The second is augmentation. This is where AI doesn't replace expertise but amplifies it. Think of strategy team running hundreds of scenario models in hours instead of weeks. R&D leaders using AI to scan thousands of patents and journals to identify the wide space for innovation. The value here is the speed to insight and breadth of perspective. The leadership question shifts from how do we train our teams to use AI as an intellectual partner, not just as a digital assistant. The third is agency. This is a frontier. AI agents operate with goals, tools, and a degree of independence. Supply chain has already been testing autonomous agents that can monitor disruption, reroute the shipments, and escalate only when thresholds are breached. In banking, agents are being piloted to manage liquidity in near real life. At this time, the leadership focuses is governance. What boundaries, accountable structure, trust framework must we build to let AI act without constant human oversight? For leaders like you, the real decision is not can AI do it, but rather, which mode is right for the business outcome we're targeting, and how do we enable it responsibly? Now that we have framed the three modes, let's discuss how leaders can decide when to use each one and how to prepare their organization to shift gears between them. 11. Key Points LLM: If I have to do a simple comparison summary or a quick wrap up of different types of LLMs, OpenAI is best for reasoning but at higher cost. Anthropic is the safest output, sometimes too cautious. Google Gemini is the best Multi model features. It is tied to the Google ecosystem. Metaama is most flexible and open source, but it needs technical skills. Mistral is efficient and lightweight, still early in adaption. Chair at strong enterprise data and Ag, but it is not known to common people. Grog is new and experimental, not yet proven. I will see you in the next class. 12. Thank you for choosing Understanding LLMs: And with that, we have reached the end of understanding leading large language models. I want to sincerely thank you for being my student and for completing this class with me. You didn't just learn what is large language model. You learned how to build a decision mindset. You now understand how large language models differ from traditional softwares. You explore the major players in the AI landscape. We together examine the strength, limitations, cost, control, compliance, and data consideration while selecting the large language model. And most importantly, we learned how to choose the right model for the right use. That is a leadership skill because in today's AI driven world, the real advantage doesn't come from using the tools blindly. It comes from making informed responsible decisions. Let me share a bit about who am I beyond this class. Hi, I'm Dimple Sanghvi, instructional designer, AI capability builder, corporate trainer, and founder of Aviza Learning App. Over the years, I have worked with professionals, managers, and organizations across industries to build structured capability in AI adoption, operational excellence, lean six sigma, and digital transformation. My focus has always been practical applications, no hype, no jargons. I don't create fear but help you get clarity about the change. I help individuals and team move from confusion about AI or digital transformation to confidence in decision making. Whether you are a business leader, evaluating AI tools, a consultant, advising clients or a professional trying to integrate AI into your workflow, this class is part of your larger journey towards capability building. And I would love to stay connected with you. You can connect with me on LinkedIn, where I share structured insights on AI strategy, model comparison frameworks, productivity systems and responsible AI implementation. You can also join my Whatsapp channel for short actionable AI insights and micro learnings and some virtual instructor led programs. These micro learning content are designed for busy professionals, and if you would like to get some deeper structured understanding, you can download my Aviza learning app from the Google Play Store where you'll find additional courses, frameworks, templates, and guided learning pathways designed to build real world AI capability. AI models will continue to evolve, new versions will launch. Capability will expand. Cost will shift, but your ability to evaluate, compare, and choose wisely, that's what keep you relevant. I want to pause for a moment and say something simple but sincere. Thank you. Thank you for being my students, and thank you for investing your time with me. You didn't just watch the lessons, you engaged with me. Keep thinking critically, keep choosing intentionally, and keep building the capabilities, not just familiarity. I will see you in the next course.