Throw more AI at your problems
Last week, we published our longest-ever post outlining how we think the AI market will evolve. The feedback on that post has been super positive so far — we’d love to hear what you think of it! This week, we’re going in the exact opposite direction with a much more tactical observation about how you should build AI applications.
You’re probably familiar the fundamental theorem of software engineering, which states that any problem can be solved by introducing an extra level of indirection. At RunLLM, our fundamental theorem of AI applications is that any problem can be solved by an extra LLM call. In other words, one of our favorite solutions is to throw more AI (LLM calls) at our problems. This may sound a little silly, but we’ve found that it’s a remarkably good heuristic for us.
Before we dive into explaining why and how this works, a little bit of background is in order. Way back in 2023, when everyone was still figuring out what to do with LLMs, there was a lot of discussion about what the best technique for building AI applications was. The most common debate was whether RAG or fine-tuning was a better approach, and long-context worked its way into the conversation when models like Gemini were released. That whole conversation has more or less evaporated in the last year, and we think that’s a good thing. (Interestingly, long context windows specifically have almost completely disappeared from the conversation though we’re not completely sure why.)
