Context Window Testing
Context window testing verifies how a model behaves as the amount of information in its context (conversation history, retrieved documents, system instructions) approaches or exceeds its maximum context length — checking for degraded accuracy, forgotten instructions, or dropped information as context grows large.
Models don't necessarily use their full context window with uniform attention — a well-documented phenomenon sometimes called "lost in the middle" shows many models pay less attention to information placed in the middle of a long context compared to the beginning or end, even when technically well within the stated context limit.
Testing typically involves deliberately placing critical information at different positions within a long context and checking whether the model still retrieves and uses it correctly — a system relying on long conversation history or large retrieved documents needs this validated specifically, not assumed just because the content technically fits within the token limit.
