✨ LLMs Today: What They Actually Do
Modern LLMs aren’t omniscient. They’re pattern matchers with a knack for language. But what they *can* do — draft, summarize, brainstorm, debug — is already valuable. This article explores real use cases, not hype.
Beyond these basic functions, LLMs are already transforming workflows across industries. In customer support, they power chatbots that handle complex queries with context awareness, reducing response times from hours to seconds. In software development, they suggest code completions that anticipate the next logical step, increasing developer productivity by 30-40% in some studies.
In content creation, LLMs help writers overcome blocks by generating multiple variations of a paragraph, which can then be refined. They're also being used for rapid prototyping of marketing copy, where multiple iterations can be generated and tested in minutes rather than days. In research, they help scientists sift through thousands of papers to identify relevant findings and summarize key insights.
The most effective use of LLMs isn't in replacing human work, but in augmenting it. By handling the tedious, repetitive aspects of creative and technical work, they free humans to focus on higher-level thinking, strategic planning, and creative direction. This "co-pilot" model, where humans provide the vision and LLMs handle the execution, is where the real value lies.
The 80% Rule: Accepting Imperfection
Most AI outputs are “good enough.” The goal isn’t perfection — it’s speed, iteration, and direction. We’re not waiting for the perfect model. We’re using the one we have.
Private LLMs
What is the reason to use small LLMs? Like 4B? 32B models?
