Context Compression
Context Compression
A technique that shrinks long text or conversation history fed to an AI while preserving key information.
In Simple Terms
Context compression is a technique that shrinks long text or conversation history fed to an AI, squeezing it down while keeping the key points intact. It's used in things like chat features that summarize and remember long past conversations, or systems that search through huge piles of documents to answer questions. By trimming information down to size, it keeps everything within the AI's processing limits so it can respond smoothly.
Behind the Name
"Context Compression" combines "context" — the surrounding text or background information an AI reads — with "compression," the act of shrinking something down. Since AI models can only take in a limited amount of data at once, the name reflects the idea of condensing all that context into a smaller, more manageable size while keeping its essential meaning intact.
Take a Closer Look!
Context compression is a technique that organizes the text or conversation history fed into a generative AI and shrinks that data down to a more compact size.
Think of it like summarizing a thick book or cutting out the unnecessary parts to create a set of concise notes covering just the key points.
AI models have a limit on how much text or how many words they can take in at once.
If you hand over text that's too long, the AI might not be able to process all of it, or it could end up costing more time and computing power.
So, this technique removes lower-priority words from the text or rewrites long content into shorter summaries to cut down on the amount of data.
Using this technique, conversation histories that have grown long over many exchanges, or text gathered through methods like RAG, can be tidied up and shrunk down to fit within the input limit before being handed to the AI.
Keeping the text shorter helps stay within token limits and reduces computing costs, which boosts the AI's processing speed and eases its workload.