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How do you evaluate long-context models (needle-in-a-haystack and beyond)?

A model advertising a million-token window may not use it. The signal is knowing needle-in-a-haystack, where it breaks, and how to test real multi-fact reasoning across the full context. Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A model advertising a million-token window may not use it. The signal is knowing needle-in-a-haystack, where it breaks, and how to test real multi-fact reasoning across the full context. Here is the answer.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·How language models work, enough to build onFree13mA big context window does not mean you should fill itContext windows keep growing, and the instinct is to stop thinking about what goes in them. This lesson closes the module by showing why capacity is not recall, what actually degrades as the window fills, and how to decide between more context and better context.
UP NEXT ON YOUR JOURNEY
Next in this trackHow do you distill a large LLM into a smaller one, and what are the approaches?Next in this trackGiven a new LLM use case, how do you decide between prompting, RAG, fine-tuning, and tools/agents?Next in this trackWhat are Matryoshka embeddings, and why are they useful for retrieval at scale?
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