Context Window & Context Engineering
Understanding how LLMs process information and the art of designing optimal context for AI systems
What is a Context Window?
The context window is the amount of text an LLM can process and "remember" at any one time to generate a response. Think of it as the model's working memory—it enables the AI to maintain coherence and relevance over a conversation or when analyzing long documents.
Everything the model considers for a given turn is included: the user's prompt, system instructions, retrieved documents, conversation history, and even the text the model is generating.
Key Insight: The context window is not storage—it's working memory. Information outside the window is "forgotten" by the model.
How Tokens Work
Text is broken down into smaller units called tokens. These can be words, parts of words, or even punctuation. For English text, approximately 0.75 words equal one token, or about 4 characters.
⚠️ Important: The efficiency of tokenization directly impacts how much content fits into the context window. Complex characters or non-English text may use more tokens.
Token Limits by Model
Context window sizes have expanded dramatically across leading LLMs. Here's how the major models compare:
| Provider | Model | Context Window | Notes |
|---|---|---|---|
|
OpenAI
|
GPT-4o / GPT-4 Turbo | 128K | Standard API access |
| GPT-4.1 (API) | 1M | Developer API access | |
| GPT-5 | 128K-400K | ChatGPT reserves ~800 tokens for system | |
|
Anthropic
|
Claude 3.5 Sonnet / Opus | 200K | Standard access |
| Claude 4 / 4.5 | 1M (beta) | Enterprise/advanced tiers | |
|
Google
|
Gemini 1.5 Pro / 2.0 Flash | 1M | Up to 2M in some configs |
| Gemini 2.5 Pro | 1M | Multimodal: 1hr video, 11hr audio |
⚠️ "Lost in the Middle" Problem: Even with large context windows, LLMs can struggle to effectively use information buried in the middle of long texts, even if it's technically within the context.
What Goes into the Context Window?
The context window contains all information the model processes for a single request. Understanding its composition is crucial for effective context engineering.
Context Engineering
Context engineering is the practice of designing, selecting, and structuring all information provided to an LLM—including instructions, inputs, retrieved knowledge, tool outputs, and memory—so the model can reason correctly for a specific task.
Key Distinction: Prompting tells the model what to do. Context engineering controls what the model can think with.
Context Engineering Architecture
Context engineering isn't a single prompt—it's an architecture that controls how information flows into the LLM. Here are the 6 essential layers:
Context Sources Layer
Where raw information comes from: system rules, user input, retrieved documents (RAG), tool/API outputs, short-term memory, long-term memory. At this stage, everything is unfiltered and noisy.
Context Selection Layer
Not everything should reach the model. This layer decides: what is relevant to the current task, what must be excluded, and how much information is enough. This is where most production systems fail.
Context Structuring Layer
Selected context is now organized, not dumped. Typical structure: System instructions → Task definition → Constraints → Knowledge/data → Tool results → Final query.
Context Ordering & Prioritization
Order controls attention. High-priority information goes first: Rules before data, Task before history, Fresh context before old memory.
Context Window Management
The architecture must control: token limits, chunk sizes, and truncation strategy. Remember: context window is not storage—it's working memory.
Feedback & Refinement Loop
After generation: validate output, detect missing or noisy context, refine selection rules. Context engineering improves iteratively, not once.
Best Practices
-
Context Pruning
Remove irrelevant or outdated content to reduce noise and costs.
-
Stay Within Effective Context Length
Performance degrades before hitting the max limit. Aim for the "effective" window size.
-
Segmentation & Summarization
Divide large texts into meaningful units and distill their essence.
-
Isolate Context by Task
Provide each task with only the specific, relevant information it needs.
-
Use Layered Memory
Separate working memory, short-term memory, and long-term memory to optimize each independently.
-
Start Simple, Evolve Deliberately
Begin with basic prompts and add complexity only when demonstrably necessary.