When dealing with LLM context window limits, under what specific conditions would 'condensation' of retrieved information be preferred over 'summarization'?

When dealing with Large Language Model (LLM) context window limits, the choice between 'condensation' and 'summarization' depends critically on the nature of the information required by the LLM for its subsequent task. The LLM context window refers to the maximum amount of text, measured in tokens, that the model can process or 'see' at any given moment. Exceeding this limit results in truncation of input and loss of information. 'Summarization' is the process of reducing a text to its main points or key ideas, providing a high-level overview of the content. The output is typically coherent, readable prose that captures the essence of the original, often rephrasing or abstracting details to achieve brevity. Its primary goal is to convey the overall gist or narrative without needing every specific detail. 'Condensation,' in this context, is the process of extracting the most critical, dense, and specific factual information from a text, prioritizing the preservation of exact details, entities, relationships, and precise statements. Unlike summarization, which aims for narrative flow and general understanding, condensation focuses on maximizing information density and fidelity to the original data points, even if the resulting output is less fluid or comprehensive as a narrative. It often results in a collection of highly specific facts, precise figures, or key attributes. 'Condensation' is preferred ove....