Evolution of Large Language Models (LLMs): From Trasnformers to Agentic AI — Part 2

“We are moving from AI that generates text to AI that thinks, retrieves, and acts.”

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Early AI models relied on pattern-matching techniques to generate text. They were excellent at completing sentences but struggled with logical reasoning, problem-solving, and accessing real-world knowledge. Now, we are witnessing the emergence of AI systems that can reason through complex tasks, fetch external data, and act autonomously — bringing us closer to truly agentic AI. As mentioned in my previous article of this series, I will be covering the following topics in this part:

• Reasoning Step-by-Step using Chain-of-Thought Prompting
• How Retrieval-Augmented Generation (RAG) are enhancing LLMs with external knowledge
• How Agentic AI is revolutionizing autonomy

1. Chain-of-Thought Prompting

Chain-of-Thought (CoT) prompting is a technique that forces AI to break down problems into logical steps, much like a human would. Instead of answering immediately, the model is encouraged to explain its reasoning step-by-step.
Early language models struggled with complex, multi-step problems, often providing shallow or incorrect answers because they relied purely on pattern matching rather than structured reasoning. These models lacked the…