Gemini 2.5 Pro supports a 2-million-token context window, allowing you to upload roughly 20,000 pages of text in a single prompt. However, simply dumping a raw, unstructured GitHub repository or a folder of PDFs into the prompt results in degraded information recall. To reliably extract complex data from massive inputs, you must structure your documents using XML tags and enable the Context Caching API.
Here is the data-backed approach to managing 2 million tokens in March 2026 without losing critical information in the middle of your prompt.
The "Lost in the Middle" Reality
When you push a model past 500,000 tokens, attention mechanisms struggle to weigh the importance of data buried in the center of the prompt. Stanford researchers documented this in the Lost in the Middle paper, proving that LLMs recall the beginning and end of a prompt perfectly but drop facts located in the middle.
While Google claims a 99.8% Needle-In-A-Haystack (NIAH) recall rate for the Gemini Pro architecture, developer benchmarks show that multi-step reasoning across 1.5 million tokens drops to 82% accuracy unless the prompt is explicitly structured.
Step-by-Step: Structuring Massive Context
To fix retrieval degradation, you must map your unstructured data. Gemini is specifically trained to recognize and prioritize XML-formatted boundaries.
Step 1: Wrap Individual Documents Do not paste 50 text files consecutively. Wrap each file in semantic XML tags that include metadata like the filename and date.