Context compression is a technique that intelligently reduces the size of AI conversation history while preserving critical information. It determines what to keep verbatim, what to summarize, and what to discard based on importance and recency. For businesses, this means AI assistants that maintain coherent conversations even when history exceeds token limits. Without it, AI loses context mid-conversation.
You feed the AI your entire 47-page process document to answer one question.
The response ignores the relevant section and hallucinates something that sounds plausible.
The document had the answer. But buried in 47 pages, the AI never found it.
More context does not mean better answers. It often means worse ones.
INTELLIGENCE LAYER - Makes AI systems work better by giving them less to work with.
Context compression takes information that would be sent to an AI and reduces its size while preserving the parts that actually matter for the task at hand. A 10,000-word document becomes 500 words of distilled relevance.
The goal is not summarization for humans. It is optimization for AI understanding. Different techniques work for different scenarios: extractive methods pull out key sentences, abstractive methods rewrite content more concisely, and semantic methods identify which chunks are most relevant to the specific question.
AI models have limited attention. Just like humans, when you give them too much information, they lose focus on what matters. Compression is not about saving tokens. It is about improving accuracy.
Context compression solves a universal problem: how do you give someone (or something) enough information to act without overwhelming them? The same pattern appears anywhere information must be distilled for decision-making.
Start with more information than needed. Identify what is relevant to the specific task. Remove or condense the rest. Deliver focused context that enables better decisions.
Retrieval found 6 chunks about support procedures. Select a compression method to see what gets sent to the AI.
Customer escalations follow a 3-tier process. Level 1 is handled by support reps with a 24-hour SLA. Level 2 goes to team leads with a 4-hour SLA. Level 3 requires manager intervention with a 1-hour SLA.
The support team was established in 2019 following our Series B funding. Initially we had 3 support reps, and now the team has grown to 47 members across three time zones.
Escalation criteria: Customer has contacted support 3+ times about the same issue. Customer explicitly requests escalation. Issue impacts multiple users or critical functionality.
Our support ticketing system uses Zendesk Enterprise. Tickets are auto-assigned based on category and agent availability. Average first response time is 2.3 hours.
For Level 2 escalations, team leads have authority to issue refunds up to $500, extend trial periods by 30 days, or provide one-time feature unlocks.
The quarterly support review meeting happens on the second Thursday of each quarter. All team leads are required to attend. Meeting notes are stored in Confluence.
Pull out the key sentences
Score each sentence or paragraph by relevance to the question. Keep the highest-scoring segments, discard the rest. The output uses the original language, just less of it.
Rewrite to be more concise
An LLM reads the content and produces a shorter version that captures the same meaning. Three paragraphs become one. Redundant points merge. The output is new text, not excerpts.
Only keep what is relevant
Compare each chunk to the question using embeddings. Only chunks above a similarity threshold pass through. Unrelated content never reaches the AI, no matter how interesting.
Answer a few questions to get a recommendation tailored to your situation.
What type of content are you compressing?
The ops manager asks this question. Retrieval finds 8 relevant documents totaling 40,000 tokens, but the model only has room for 8,000. Context compression distills those documents to what actually answers the question, ensuring the AI sees the right information.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You summarize the entire document in advance to save on tokens. But the summary drops the section that happens to answer the specific question asked. Now the AI has no way to find the answer.
Instead: Compress relative to the question. What is irrelevant to one question may be essential to another.
The LLM rewrites your pricing policy to be more concise. It changes "$99 per month billed annually" to "approximately $100 monthly." Now the AI gives slightly wrong answers about pricing.
Instead: Use extractive methods for facts, figures, and policies. Reserve abstractive compression for explanatory content.
You set the target to 200 tokens no matter what. A complex technical process that needs 500 tokens to explain gets squeezed into fragments. The AI now has pieces but not the picture.
Instead: Set compression targets based on content complexity, not arbitrary limits. Some topics need more context.
Context compression reduces AI conversation history to fit within token limits while preserving essential information. It works by classifying content into tiers: critical information is kept verbatim, important context is summarized, and routine exchanges are heavily compressed or discarded. This allows AI systems to maintain coherent conversations even with extensive history.
Use context compression when your AI conversations regularly exceed context window limits. This typically happens with customer support bots handling long ticket histories, AI assistants that need to reference past decisions, or any system where conversation quality degrades as history grows. If your AI starts "forgetting" earlier parts of conversations, you need compression.
The most common mistake is compressing everything the same way. Legal agreements need precision preserved while casual messages can be heavily summarized. Another mistake is pure recency bias, keeping recent messages regardless of importance. A status update from today shouldn't override a critical decision from last week.
Extractive compression selects and keeps original sentences verbatim, removing less relevant ones. Abstractive compression rewrites content into shorter forms using different words. Extractive is faster and preserves exact wording for facts and policies. Abstractive produces more fluent output but risks changing meaning. Most systems combine both approaches.
AI models have limited attention that spreads across all input tokens. When context is bloated with irrelevant information, attention dilutes and the model may focus on tangential content instead of what matters. Compression increases information density, giving the model a clearer signal. Research shows 50% compression typically preserves meaning while improving response quality.
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Choose the path that matches your current situation
You have not implemented any compression yet
You are doing some filtering but results are inconsistent
Compression is working but you want better quality
You have learned how to reduce context size while preserving what matters. The natural next step is understanding how to manage the overall context window and budget tokens effectively.