You can incorporate external knowledge into an LLM by giving it access to information that isn’t part of its original training. The most common way to do this is retrieval-augmented generation (RAG). In simple terms, instead of the model guessing from memory, you let it search your documents, database, or API first, then use that information to answer. It’s like giving the model a quick reference guide before it responds.
Another way is fine-tuning, where you train the model on your own examples so it learns your specific style, rules, or domain knowledge. This is useful when you want the model to follow a certain pattern or understand very niche topics, and it’s something that comes up a lot in LLM development work.
You can also use tools or plug-ins, where the model calls external systems, for example, asking a calculator for math, a search API for live data, or a knowledge base for facts. The model doesn’t store the info itself; it just knows how to fetch it.
Overall, the idea is simple: instead of relying only on what the LLM already knows, you connect it to the right sources so it can pull in accurate, updated, or specialized information whenever needed.
Top comments (0)