It's hard to capture the full intent of our inquiry and communicate it to the AI in a single prompt. Primary sources help by capturing a density of data for the AI to reference.
Depending on the use case you are operating in, references may serve to guide a generative outcome, or be the foundation by which the AI generates its response.
As a guide
We know that AI uses tokens, contained in data, to understand the user's intent through their prompt and return something that sufficiently matches that intent. References empower the user by giving the AI a clear depiction of what they are looking for. This makes the AI's job easier in turn, as it can use that reference as an initial filter to focus its data reference before building its response.
Not all uses of references need to be focused on obtaining predictable outcomes. Multi-media generators often allow you to upload a sample reference, which can guide the style, tokens, or both that the AI uses to generate your prompt. This is especially fun with inpainting and can result in unpredictable results of editorial quality.
As a source
Sometimes the reference acts as the subject of the prompt itself, such as when a user might ask the AI to summarize, synthesize, etc. These are the use cases where the lines between primary references and general references start to blur. The distinction is that a primary source is the object that the AI is explicitly interacting with, whereas references operate as a layer on top of the LLM - much like I might ask the AI to summarize some topic but include any internal resources at first.
In these situations, the user expects to be able to upload some document and have the AI answer questions about it specifically, or offer specific suggestions for how to change it. The describe function of image generators is an example of this happening explicitly, while Adobe PDF's AI functionality is a good example of blending Copilot chat with the central object.
