Most organizations do not build large language models from scratch. They customize existing models.

Three practical customization options

1) Prompting

Prompting is the simplest entry point. It relies on well-crafted instructions to guide the model. It is limited by context window and by what the model already “knows.”

Prompt engineering is a practical way to guide outputs without changing model parameters, and it is usually the fastest place to start.

2) Retrieval-Augmented Generation (RAG)

RAG combines prompting with a knowledge base. The model answers using relevant context retrieved from your documents and systems.

RAG is often effective because it can use new information without changing the underlying model.

At a high level, the pattern is simple: retrieve relevant context, then generate the answer using the prompt plus that context.

In practice, RAG quality depends heavily on the availability and quality of the external knowledge base used for retrieval.

3) Fine-tuning

Fine-tuning further trains a model on your data. It can help when you need specialized language, consistent tone, or strong domain adaptation. It requires high-quality data and careful control.

Fine-tuning outcomes depend heavily on the quality and specificity of the training data, and the model will become tailored to the dataset it was trained on.

Model selection cheat sheet

Most teams succeed faster when they treat model choice as a product decision, not a novelty contest. Use this table to compare best-fit use cases, distinctive strengths, and the practical trade-offs around context size, security and compliance, and customization options.