The race for larger context windows in large language models (LLMs) often suggests a future where AI can process and perfectly recall entire books or codebases in a single prompt. While models now boast context windows of up to one million tokens, research and practical application reveal a critical distinction: a bigger window does not guarantee better memory. [3][4]

The promise and problem of large context windows

Larger context windows allow models to ingest and process vast amounts of information at once, but this capability does not translate to perfect recall. [2] The expansion from a few thousand to over a million tokens, as seen in models like Google’s Gemini 1.5 Pro, unlocks new potential for analyzing long documents, summarizing videos, or debugging extensive codebases. [3]

However, performance tests show that as the amount of context grows, a model's ability to find and use a specific piece of information buried within that context declines. [4] This creates a "needle in a haystack" problem, where the model knows the information is present but struggles to locate it, especially when the surrounding text is irrelevant to the immediate task. [1]

What is a context window?

A context window is the amount of text, measured in tokens, that a large language model can process as input at one time to generate a response.

Why models forget: The "lost in the middle" effect

Research shows that LLMs recall information from the beginning and end of a context window much more accurately than information from the middle. This phenomenon, often called the "lost in the middle" effect, has been documented in a key study by researchers from Stanford University, UC Berkeley, and Samaya AI. [4]

The study evaluated leading models by placing a specific fact (the "needle") at various points within a long, distracting document (the "haystack") and testing the model's ability to retrieve it. The results consistently showed a U-shaped performance curve: when the key fact was placed at the start or end of the document, recall was high; when it was placed in the middle, recall rates dropped significantly—sometimes to near zero. [4] This suggests that even with massive context windows, the model's attention is not distributed evenly; positional biases cause it to over-index on the prompt's introduction and conclusion, making the middle a blind spot for critical details. [4]

The hidden costs of bigger windows

Beyond recall issues, using large context windows incurs significant practical costs. The self-attention mechanism, a core component of the Transformer architecture used by most LLMs, scales quadratically with the length of the input sequence—so doubling context can roughly quadruple compute, raising costs and latency. [1] This increased latency can degrade the user experience in interactive applications like chatbots. [3] Furthermore, flooding a model with excessive, irrelevant context can act as noise that distracts from the primary instruction, lowering output quality; providing a smaller, more relevant context is often more effective. [1]

How to improve model recall: A practical guide

Instead of relying solely on massive context windows, organizations should implement strategies to provide models with concise, relevant information. This approach improves accuracy, reduces costs, and minimizes latency. [1][2]

1. Optimize your prompt structure

The easiest and most immediate action is to structure your prompts strategically. Since models remember the beginning and end of a context window best, place your most important instructions and data points in these positions. [4]

For example, when asking an LLM to summarize a long meeting transcript, place the instructions at the top and the key questions or required output format at the very bottom. The transcript itself can occupy the less reliable middle section. This frames the task clearly and keeps the primary goal in the model's "attentional sweet spots." [4]

2. Implement Retrieval-Augmented Generation (RAG)

For applications requiring recall from a large knowledge base, RAG is the most robust solution. RAG pre-filters information for relevance before it ever reaches the LLM. [1]

A retrieval system searches your knowledge base (e.g., internal documents, customer support articles) to find a small number of text chunks most relevant to the user's query. Second, it passes only that curated, highly relevant information into the LLM's context window along with the original query to generate an answer. [1]

This makes the "haystack" much smaller and more targeted, ensuring the "needle" is easy for the model to find and use accurately. It also allows you to cite sources for the generated answers, improving trust and verifiability. [1][2]

What is Retrieval-Augmented Generation (RAG)? 

Retrieval-Augmented Generation is a technique that combines a retrieval system, which finds relevant information from an external knowledge source, with a generative LLM to produce more accurate and contextually grounded answers. 

3. Run your own 'needle in a haystack' tests

Do not take vendor claims about context window performance at face value. Conduct your own simple evaluations to understand how a model performs on your specific data. [4]

• Create a test document: Take a long document relevant to your use case.

• Insert a "needle": Place a unique, specific fact or instruction at various points within the document (e.g., at 10%, 50%, and 90% of the way through). [4]

• Query the model: Ask a question that can only be answered by retrieving that specific fact.

• Measure performance: Run this test multiple times, varying the position of the needle. This will give you a realistic baseline for the model's recall capabilities and help you identify its blind spots. [4]

Why this matters for your AI strategy

Context window size is a vanity metric if not paired with strong recall benchmarks. De-risk initiatives by prioritizing task-specific accuracy, cost, and latency over raw input capacity—and by investing in robust RAG pipelines. [1]

The focus is shifting from prompt engineering alone to building effective retrieval systems (chunking, embeddings, vector search) that overcome inherent memory limits and deliver reliable results. [1][4]