Step 1: Vector Similarity Search

When a user asks an AI search engine a question, the query is converted into a high-dimensional vector. The system scans a database to find website chunks (paragraphs or pages) with vectors pointing in the exact same mathematical direction. If your content comprehensively matches the linguistic pattern of the answer, it is retrieved.

Step 2: Context Window Filtering

AI models can only 'read' a certain amount of text at once (the context window). The system employs an initial ranking model (like a cross-encoder) to score and filter the retrieved documents. Documents with the most direct, concisive answers score the highest and survive the cut to enter the LLM's prompt.

Step 3: Alignment and Safety Checks

LLMs are fine-tuned to avoid generating harmful or highly contested information. They implicitly prefer sources that project an objective, neutral tone and utilize verified entity references. Emotional, biased, or highly sensational text is often discarded by the attention mechanism.

Step 4: Citation Generation

As the model generates the text token-by-token, it maps its generation to the source documents in its context window. It appends a citation link to whichever document most explicitly provided the factual basis for the generated sentence. This is why having explicit factual density is paramount.