Advancing AI Systems: From LLMs to RAG, Streamlining AI Workflows and Agents

AI agents are a trending topic, but not all AI systems need to be autonomous. Simpler solutions often provide better cost savings and efficiency for various applications. In this post, we’ll delve into Large Language Models (LLMs) and their evolving capabilities. We classify LLMs into different categories, such as pure LLMs, which excel in tasks involving static knowledge but lack real-time information, and Retrieval Augmented Generation (RAG), which enhances LLMs with current, relevant context. LLMs can also automate structured tasks by integrating with APIs for operations like resume screening. At a higher level, AI agents can reason and make decisions independently, orchestrating complex tasks without human prompts. The key takeaway is that not all applications require an AI agent; simpler solutions may suffice, and focusing on reliability is paramount for building dependable systems. Start with basic approaches and scale complexity only as needed.

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