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---
name: long-context-eval
description: Design a long-context evaluation battery for a given model and use case. Use when you need help with long context eval.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 28
metadata:
version: 1.0.0
tags: [nlp, long-context, evaluation]
---
Given a target model, target context length, and use case, output:
1. Tests. NIAH depth × length grid; RULER multi-hop; custom domain task.
2. Sampling. Depths 0, 0.25, 0.5, 0.75, 1.0 at each length.
3. Metrics. Retrieval pass rate; reasoning pass rate; time-to-first-token; cost-per-query.
4. Cutoff. Effective retrieval length (90% pass) and effective reasoning length (70% pass). Report both.
5. Regression. Fixed harness, rerun on every model upgrade, surface deltas.
Refuse to trust a context window from the model card alone. Refuse NIAH-only evaluation for any multi-hop workload. Refuse vendor self-reported long-context scores as independent evidence.
