Large language models like GPT‑4 cap their context window at a few thousand tokens, forcing developers to truncate or summarize long inputs. A new article on Towards Data Science demonstrates a recursive language model technique that feeds a model’s own output back as input, effectively stitching together arbitrarily long texts.

The recursion works by dividing a document into overlapping chunks, prompting the LLM on each slice, then feeding the summary of the previous slice into the next prompt. This preserves continuity across boundaries without exceeding the token limit, enabling analysis of massive datasets such as legal contracts or scientific corpora in a single pipeline.

Practitioners can implement the pattern with existing APIs, using simple prompt chaining scripts rather than custom model fine‑tuning. As community benchmarks emerge, watch for open‑source libraries that automate chunking and state‑passing, and for research measuring latency versus accuracy trade‑offs. Recursive approaches may soon become the default for any task exceeding native context window sizes.