client wants context window guarantees, how do you even measure this reliably

can you post the actual measurement methodology? sliding window perplexity or just spot checks?

We measure perplexity on 5 representative samples at different context lengths (8k, 16k, 24k, 32k) and guarantee coherence up to the point where perplexity crosses 12.0. It's not perfect but it's repeatable. What's your current measurement approach - are you using fixed prompts or dynamic?

you can't give hard context window guarantees because coherence degradation is content-dependent, not just token-count-dependent. tested this extensively with llama 3.3 70b - nested json breaks at 23k, flat json holds to 28k, code with explicit state machines holds longer than implicit control flow. best you can do is measure perplexity curves on representative samples of your actual content type and give probabilistic bounds with caveats

We measure context window reliability with sliding window perplexity at 2k intervals (8k, 10k, 12k...up to 32k) on 10 representative samples from actual production traffic. Track mean + p95 perplexity at each window size. Guarantee coherence up to the point where p95 perplexity crosses our threshold (we use 11.5 for llama 3.3 70b q4_k_m based on 6 months of client feedback). It's expensive to compute but repeatable and defensible. The key is using real production samples not synthetic - synthetic data always overestimates the usable context window by 15-20% in our testing.