From LLM to AI Agent: What's the Real Journey Behind AI System Development?

Summary

AI agents are a hot topic, but not every AI system needs to be one. While agents promise autonomy and decision-making power, simpler & more cost-saving solutions better serve many real-world use cases. The key lies in choosing the right architecture for the problem at hand. In this post, we'll explore recent developments in Large Language Models (LLMs) and discuss key concepts of AI systems. We've worked with LLMs across projects of varying complexity, from zero-shot prompting to chain-of-thought reasoning, from RAG-based architectures to sophisticated workflows and autonomous agents. This is an emerging field with evolving terminology. The boundaries between different concepts are still being defined, and classifications remain fluid. As the field progresses, new frameworks and practices emerge to build more reliable AI systems. To demonstrate these different systems, we'll walk through a familiar use case – a resume-screening application – to reveal the unexpected leaps in capability (and complexity) at each level. Pure LLM A pure LLM is essentially a lossy compression of the internet, a snapshot of knowledge from its training data. It excels at tasks involving this stored knowledge: summarizing novels, writing essays about global warming, explaining special relativity to a 5-year-old, or composing haikus. However, without additional capabilities, an LLM cannot provide real-time information like the current temperature in NYC. This distinguishes pure LLMs from chat applications like ChatGPT, which enhance their core LLM with real-time search and additional tools. That said, not all enhancements require external context. There are several prompting techniques, including in-context learning and few-shot learning that help LLMs tackle specific problems without the need of context retrieval. Example: To check if a resume is a good fit for a job description, an LLM with one-shot prompting and in-context learning can be utilized to classify it as Passed or Failed. RAG (...