Phase 4: NLP Applications · ~60 minutes · Python

Long-Context Evaluation — NIAH, RULER, LongBench, MRCR

Gemini 3 Pro advertises 10M tokens of context. At 1M tokens, 8-needle MRCR drops to 26.3%. Advertised ≠ usable. Long-context evaluation tells you the actual capacity of the model you are shipping on.

What you will learn

Implement long-context evaluation — niah, ruler, longbench, mrcr from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Learn
Languages: Python
Prerequisites: Question Answering Systems, Chunking Strategies for RAG
Time: ~60 minutes

The Problem

You have a 200-page contract. The model claims a 1M-token context. You paste the contract in and ask: "What is the termination clause?" The model answers — but answers from the cover page because the termination clause sits at 120k tokens deep, past where the model actually attends.

This is the 2026 context-capacity gap. Spec sheets say 1M or 10M. Reality says 60-70% of that is usable, and "usable" depends on the task.

Retrieval (single needle in haystack): near-perfect up to the advertised max on frontier models.
Multi-hop / aggregation: degrades sharply past ~128k on most models.
Reasoning over dispersed facts: the first task to fail.

Long-context evaluation measures these axes. This lesson names the benchmarks, what each actually measures, and how to build a custom needle test for your domain.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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