Have you ever asked Four paths to performance and qualityWhen it comes to improving LLM performance and output quality, I group the approaches into four key categories:Prompt engineering and in-context learningRetrieval-augmented generation (RAG)Fine-tuning foundation modelsBuilding your own model from scratchLet’s look at each one.1. Prompt engineering and in-context learningPrompt engineering is all about crafting specific, structured instructions to guide a model’s output. It includes zero-shot, one-shot, and few-shot prompting, as well as advanced techniques like chain-of-thought and tree-of-thought prompting.Sticking with our healthcare analogy, think of it like giving a detailed surgical plan to a neurosurgeon. You’re not changing the surgeon’s training, but you’re making sure they know exactly what to expect in this specific operation. You might even provide examples of previous similar surgeries – what went well, what didn’t. That’s the essence of in-context learning.This approach is often the simplest and fastest way to improve output. It doesn’t require any changes to the underlying model. And honestly, you’d be surprised how much of a difference good prompting alone can make.2. Retrieval-augmented generation (RAG)RAG brings in two components: a retriever (essentially a search engine) that fetches relevant context, and a generator that combines that context with your prompt to produce the output.Let’s go back to our surgeon. Would you want them to operate without access to your medical history, recent scans, or current health trends? Of course not. RAG is about giving your model that same kind of contextual awareness – it’s pulling in the right data at the right time.This is especially useful when the knowledge base changes frequently, such as with news, regulations, or dynamic product data. Rather than retraining your model every time something changes, you let RAG pull in the latest info.