A context window is the total text an LLM can see and reason over in a single call. Optimizing how you use that space — what you include, in what order, at what level of detail — directly affects output quality, latency, and cost. Even as window sizes grow, the discipline of optimization remains valuable: a focused context outperforms a bloated one at any size.
Understanding context window limits
Context window size is measured in tokens — roughly 0.75 words each in English. Modern models offer windows from 32K to 1M+ tokens. Despite this, filling the window is not the goal. Token count affects cost linearly (you pay per token processed) and affects latency significantly (larger contexts take longer to process). Including irrelevant content also dilutes model attention, which can reduce output quality.
The "lost in the middle" phenomenon is well-documented: LLMs attend more strongly to content at the beginning and end of their context than to content in the middle. For large contexts, this means your most important information should appear at the start (system context, task description, key constraints) and at the end (the immediate task), with supporting reference material in between.
Token budgeting strategies
A useful mental model: treat your context window as a budget with designated allocations. A typical coding task might look like:
- System prompt and instructions: 500–2,000 tokens
- User story or task description: 200–500 tokens
- Architecture / planning context: 1,000–5,000 tokens
- Relevant code files: 2,000–20,000 tokens
- Conversation history: up to 10,000 tokens
- Reserve for output: 2,000–8,000 tokens
Working from allocations makes overruns visible and forces prioritization. When the code files allocation is exhausted, you choose which files to include rather than including everything and hoping.
Compression and summarization
When content exceeds its allocation, compression is better than truncation. Truncating a document mid-way through may cut its most important sections. Summarizing it preserves the key points while reducing token count significantly.
Effective compression strategies: hierarchical summarization (summarize each section independently, then summarize the summaries); extractive compression (include only the paragraphs that contain the key terms relevant to the task); and schema-based compression (for structured data like API documentation, extract only the endpoint signatures and discard the prose descriptions).
For conversation history, rolling summarization works well: every N turns, compress the oldest turns into a summary paragraph that carries the essential decisions and context forward. The summary replaces the raw history, freeing space for new content.
Hierarchical context patterns
Hierarchical context organizes information at multiple levels of detail, and includes only the level needed for each task. For a large codebase: include the architecture overview (high level), the module-level design for the area being modified (mid level), and the specific files being changed (low level). Don't include the full codebase.
For project planning documents, a similar hierarchy works: include the PRD executive summary (high level), the relevant feature section (mid level), and the specific user story being implemented (low level). Each level answers different questions; including all levels ensures the model can reason from "why does this exist" to "what exactly should it do" to "how should this specific part work."
Caching for repeated queries
Many AI applications make repeated calls with similar context. Prompt caching (supported by major LLM providers) allows you to cache a prefix of the context and skip re-processing it on subsequent calls. For a customer support application where the product documentation is always included, caching the documentation portion of the prompt reduces cost and latency significantly.
KV-cache reuse at the infrastructure level also rewards stable context ordering. If your system prompt and documentation always appear in the same position, the cached KV states remain valid across calls. Varying the context order — even slightly — invalidates the cache. Structure your context composition to be deterministic.
Practical checklist
- Set explicit token budgets for each context section before composing
- Put highest-signal content first and last, supporting material in the middle
- Compress rather than truncate when content exceeds allocation
- Use retrieval to avoid including documents that aren't relevant to the current task
- Apply rolling summarization to long conversation histories
- Cache stable context prefixes (system prompt, product docs, architecture)
- Measure: track token usage per call and set alerts for budget overruns
See Context Compression Techniques for LLMs for a deeper look at compression methods, or the RAG guide for retrieval-based optimization.