Context Window Optimization: Getting More from Every Token

Context windows are not unlimited. Even the largest models have a finite number of tokens they can process at once. And here is the part most people miss: bigger is not always better.

Filling a 128K context window with everything you have does not improve output quality. It degrades it. The model spends attention on irrelevant information. Signal gets diluted by noise. Your carefully crafted prompt drowns in a sea of marginally relevant context.

The skill is not having a big context window. The skill is using it wisely.

The Attention Budget

Think of context window tokens as a budget. Every token you spend on context is a token the model processes. More tokens mean more compute, higher latency, and higher cost. But the real cost is attention dilution.

Language models do not give equal attention to all tokens. Information at the beginning and end of the context gets more attention than information in the middle. This is not a bug. It is how transformer attention mechanisms work.

Practical implication: put your most important information first (system prompt, critical instructions) and last (current query, recent context). Let less critical information occupy the middle. This is not optimization theory. This is how every production AI system should structure its prompts.

Prioritization Is the First Principle

Not all context is equally valuable. Rank your context by relevance to the current task.

Tier 1: System instructions and current task details. These are mandatory. They define what the model is doing and how. Never compress or omit these.

Tier 2: Directly relevant context. For a coding task, the files being modified. For a conversation, the recent messages. For a search query, the top retrieved documents. This context directly influences output quality.

Tier 3: Background context. Conversation history beyond the recent messages. Reference documentation. Style guides. Helpful but not essential for the immediate task.

Tier 4: Nice-to-have context. Loosely related information. Historical data. Extended documentation. Include this only when you have tokens to spare.

When context window space is tight, cut from the bottom. Tier 4 goes first. Then Tier 3 gets compressed. Tiers 1 and 2 are non-negotiable.

Compression Techniques That Work

Summarization is the obvious approach. Take a long document and generate a shorter summary. This works but loses details. Use it for background context where the gist matters more than the specifics.

Extraction is better than summarization when you know what you need. Instead of summarizing a 10-page document, extract the specific facts relevant to the current query. A 10-page document becomes 5 bullet points. Dramatic token reduction with minimal information loss.

Structured formats compress naturally. A table with 5 columns and 10 rows conveys information that would take 3 paragraphs of prose. Tables, lists, and key-value pairs are information-dense formats that models process efficiently.

Deduplication catches the waste that accumulates in conversation histories. The same information restated in different messages. The same context re-injected at multiple points. Remove duplicates and you often recover 20-30% of your context window.

Selective history keeps only the messages that matter. In a long conversation, many messages are clarifications, corrections, or tangential discussions. Trim these and keep the substantive exchanges. The model does not need to see the three messages where the user corrected a typo.

Dynamic Context Construction

Static context is wasteful. Injecting the same block of context regardless of the query means most tokens are irrelevant most of the time.

Dynamic context construction selects context based on the specific query. This is essentially RAG, Retrieval-Augmented Generation, but the principle applies beyond traditional RAG setups.

For a customer support AI, retrieve only the help articles relevant to the current question. For a coding assistant, include only the files relevant to the current task. For a research AI, retrieve only the papers relevant to the current topic.

The retrieval step costs a few hundred milliseconds and a few hundred tokens for the retrieval query. It saves thousands of tokens of irrelevant context and dramatically improves response quality. Worth it every time.

Conversation Management

Long conversations are the biggest context window challenge. Each message adds tokens. Eventually the conversation exceeds the context window and something has to give.

Sliding window: Keep the last N messages. Simple. Predictable. Loses early context that might be important. Works well for task-focused conversations where recent context is most relevant.

Summarization window: Periodically summarize older messages into a condensed form. Keep recent messages verbatim and older messages as summaries. Preserves key decisions and context while reducing token count.

Semantic selection: For each new query, retrieve the most relevant previous messages regardless of recency. A message from 50 turns ago might be more relevant than the last 3 messages. This requires embedding each message and running similarity search against the current query.

In practice, the best approach combines these. Recent messages verbatim (last 5-10). A running summary of the conversation so far. Semantically retrieved messages from earlier in the conversation when relevant.

Token Counting and Budgeting

You cannot optimize what you do not measure. Count tokens for every component of your prompt.

System prompt: how many tokens? Is it as concise as it can be without losing critical instructions?

Retrieved context: how many tokens per document? How many documents? Is this proportional to the value they add?

Conversation history: how many tokens? Where is the growth coming from? Can older messages be compressed?

Set token budgets for each component. System prompt: 500 tokens max. Retrieved context: 2000 tokens max. Conversation history: 3000 tokens max. Current query: whatever the user sends. Response: 1000 tokens max.

These budgets force discipline. When your retrieved context exceeds 2000 tokens, you retrieve fewer documents or compress them harder. When conversation history exceeds 3000 tokens, you summarize more aggressively.

The Cost Dimension

Token optimization is not just about quality. It is about money.

At current API prices, the difference between a 5000-token prompt and a 15000-token prompt is roughly 3x cost per request. At thousands of requests per day, that is significant.

Optimization compounds. A 30% reduction in context tokens means 30% less cost AND faster responses AND often better output quality. It is the rare optimization that improves every metric simultaneously.

Build token tracking into your application from day one. Monitor average prompt size, context utilization, and cost per request. Set alerts when metrics drift upward. Optimization is not a one-time effort. It is ongoing maintenance.

The Counter-Intuitive Truth

Less context often produces better outputs than more context. A focused prompt with precisely relevant context outperforms a sprawling prompt with comprehensive but diluted context.

The temptation to add more context is strong. More information should mean better answers, right? Not when the model's attention is finite and the additional information is only tangentially relevant.

Be ruthless about what goes into your context window. Every token should earn its place. If a piece of context does not directly improve the expected output, it is noise. Cut it.