🔗 Why You (Probably) Don't Need to Fine-tune an LLM

In this post, we'll talk about why fine-tuning is probably not necessary for your app, and why applying two of the most common techniques to the base GPT models — few-shot prompting and retrieval-augmented generation (RAG) — are sufficient for most use cases.

This post is targeted towards folks focused on building LLM applications (as opposed to research).

If you're a builder, it's important to know what's available in your toolbox, and the right time to use a given tool. Depending on what you're doing, there are probably ones you use more often (hammer, screwdriver), and ones that you use less often (say, a hacksaw).

A lot of very smart people are experimenting with LLMs right now — resulting in a pretty jam-packed toolbox, acronyms and all (fine-tuning, RLHF, RAG, chain-of-thought, etc). It's easy to get stuck in the decision paralysis stage of "what technical approach do I use", even if your ultimate goal is to "build an app for X".

On their own, people often run into issues with base model LLMs — "the model didn't return what I wanted" or "the model hallucinated, its answer makes no sense" or "the model doesn't know anything about Y because it wasn't trained on it".

People sometimes turn to a fairly involved technique called fine-tuning, in hopes that it will solve all of the above. In this post, we'll talk about why fine-tuning is probably not necessary for your app.

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