Inference

Full Title or Meme

Assigning an Entity present on the internet to some category for analysis.

Context

The CCPA defines Inferences: a separate category of data that includes inferences drawn from any of the categories in the CCPA legislation to create a profile about the consumer, including preferences, trends, characteristics, psychological profiles and others.

Problems

Inferences about individuals can rapidly turn into stereotypes and create harm as a result.

Artificial Intelligence

Prompt processing (or prompt interpretation) This is the broad term used to describe what happens when an LLM takes your input and turns it into internal representations it can act on—from raw text to a generated response. Many technical explanations use exactly this phrasing when walking through tokenization, embeddings, attention, and decoding. [adaline.ai], [community....bricks.com]

If you want the technical pipeline name In more precise terms, prompt interpretation is usually broken into stages rather than given a single label:

Tokenization

The prompt is split into tokens (subwords or symbols).
This step is universally named tokenization in the literature. [mdchhafrul...amkhan.com]

Embedding

Tokens are converted into numerical vectors.
This is referred to as embedding or vectorization of the prompt. [adaline.ai]

Contextual encoding / attention

The model processes those vectors using transformer attention mechanisms to infer relationships and meaning. [adaline.ai]

Inference / decoding

The model generates output by predicting the next token conditioned on the prompt.
This is usually called autoregressive decoding or inference. [community.ibm.com]

Collectively, this whole sequence is what people casually call “understanding the prompt,” even though no semantic understanding occurs in the human sense.

If you’re asking from a user‑intent perspective

 In product, search, and UX contexts, the process is often called:
Intent inference or intent parsing
This refers to how LLMs infer what the user is trying to do (ask, compare, summarize, instruct, etc.), not just the literal words. This terminology is common in applied AI and search discussions. [govisible.ai]

If you’re asking from a practice / discipline perspective You’ll often hear:

Prompt engineering
This is not what the model does. It’s the human practice of designing prompts to steer the model’s interpretation and output. It’s frequently confused with the internal interpretation process, but it’s a separate concept. [techcanva.com], [ce.icep.wisc.edu]