Context as architecture: A practical look at retrieval-augmented generation

In a previous article, The strategic choice: Making sense of LLM customization, we explored AI prompting as the first step in adapting large language models (LLMs) to real-world use. Prompting changes how an AI model responds in terms of tone, structure, and conversational behavior without changing what the model knows.That strategy is effective until the model requires specific information it did not encounter during its initial training.At that point, the limitation is no longer conversational—it is architectural.Retrieval-augmented generation (RAG) helps address that limitation. Not by ma