How often do you ask a generative AI model a long question, only for it to forget the details halfway through its answer?
This common frustration points directly to how large language models (LLMs) handle memory. Unlike human brains, these systems do not store past conversations in separate, long-term files. Every time you send a prompt, the model reads the entire history of your chat from scratch.
To make this work, the system relies on a specific technical mechanism. Enter the context window.
In this post, we will define context windows, explain how context compaction solves memory limits, and provide practical ways to make your AI operations cheaper and more accurate.
What is a Context Window?
A context window is the maximum amount of text an LLM can process and “remember” at one time when generating a response. Everything you send to the model must fit within this limit. This includes your current question, previous messages in the conversation, and any background documents.
This size is measured in units called tokens. A token is a small chunk of text. In English, one token equals about three-quarters of a word.
The context window sets the hard boundary on the amount of information the model can consider together to produce an answer. Think of it as the model's working memory for that specific interaction.
If you exceed this token limit, the model must truncate or summarize the older text to proceed.
Why Are Context Windows Important for Business?
For enterprise users, the size of a context window directly impacts what you can build.
A larger window allows the model to remember much longer conversations without losing early details. It also means you can feed the system entire long documents, large codebases, or hours of transcripts in a single prompt.
In 2022, most models handled only 2,000 to 8,000 tokens. This is roughly equal to a few pages of text. Today, common enterprise models can handle between 200,000 and 1,000,000 tokens. Some even advertise up to 10 million tokens.
Increasing an LLM’s context window size translates to increased accuracy and fewer hallucinations. It allows for more coherent responses and improves the ability to analyze long sequences of data.
But bigger is not always better.
Equipping a model with a large context window comes at a high computational cost. Compute requirements scale quadratically with the length of a sequence. If the number of input tokens doubles, the model needs four times as much processing power. This makes responses slower and API costs much higher.
There is also a performance issue known as "lost in the middle." Models pay the strongest attention to text at the very beginning and the very end of a prompt. Information placed in the middle of a very long input is often ignored or forgotten.
Because of this, many models perform much better on shorter inputs than their maximum advertised size suggests.
What is Context Compaction?
Context compaction (or compression) means shrinking the input text while keeping the most important information.
Instead of feeding a 500-page document into a massive, expensive context window, you reduce the text to its core facts. This makes prompts shorter, faster, and cheaper. It also reduces the "lost in the middle" errors because there is less text for the model to sift through.
At its core, compaction distills the contents of a context window in a high-fidelity manner. This allows the AI agent to continue working with minimal performance degradation.
How to Implement Context Compaction?
Here's 5 proven ways to implement context compaction for enterprise applications.
- Summarization: Use a fast, cheap model to create a short summary of a long chat history. You can turn 50 pages into 1 or 2 pages of key facts.
- Targeted Extraction: Filter the text to include just the sentences related to the current question. Remove off-topic content and filler words.
- Duplicate Removal: If the same information appears multiple times, keep it only once.
- Rolling Summaries: Compress old history into a brief memory summary, but keep the last 5 to 15 messages in full detail. The model remembers the big picture without a huge input.
- Structured Lists: Convert long paragraphs into concise bullet points detailing important names, dates, and decisions.
3 Tips for Managing Context Effectively
When building AI solutions, managing what goes into the model is just as critical as the model itself.
- Don't Chase the Biggest Number: Advertised context size does not equal effective size. A model claiming a 1 million token window might drop sharply in accuracy after 100,000 tokens. Focus on the quality of your input, not just the volume.
- Clear Out Tool Results: One of the safest forms of compaction is clearing tool calls and results. Once a tool has been called deep in the message history, the agent rarely needs to see the raw result again.
- Tune Your Compaction Prompts: If you are building automated compaction systems, start by maximizing recall. Make sure your prompt captures every relevant piece of information. Then, iterate to improve precision by eliminating superfluous content. Overly aggressive compaction can result in the loss of subtle but critical context.
Why This Matters for Your AI Strategy?
Context engineering represents a fundamental shift in how we build with LLMs. The challenge isn't just crafting the perfect prompt. The real challenge is thoughtfully curating what information enters the model's limited attention budget at each step.
Find the smallest set of high-signal tokens that maximize the likelihood of your desired outcome. This approach reduces API costs by 50 to 80 percent while delivering faster and more accurate answers.
As we saw in our recent BBI technical post on Data Quality and Observability, building resilient data pipelines is mandatory for AI success. The same logic applies to how you feed that data into the model.
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