You fed the AI your 47-page operations manual. You gave it the customer's full history. You included every relevant document you could find.
Now the AI ignores the most important details and invents answers for questions you explicitly covered.
The AI did not malfunction. You overwhelmed it. Context windows have limits, and you hit them.
What you include matters less than what you prioritize.
LAYER 2 INTELLIGENCE - This determines what information the AI actually uses.
Every AI model has a context window: a fixed amount of text it can 'see' at once. GPT-4 might handle 128,000 tokens. Claude might handle 200,000. But here is the reality: more context does not mean better answers. It often means worse ones.
When you dump everything into the context window, the AI faces the same problem your team faces when someone forwards a 47-email thread and says 'thoughts?' The signal gets buried in noise. Important details compete with irrelevant ones. The most recent information often wins regardless of importance.
Context window management is the discipline of deciding what goes in, in what order, and how much space each piece gets. It is triage for information. You cannot include everything, so you must choose what matters most for this specific task.
Get it wrong and the AI confidently answers questions you did not ask while ignoring the ones you did. Get it right and a fraction of the context produces dramatically better results.
Context window management solves a universal problem: when you have finite capacity and unlimited input, something has to decide what gets in and what stays out. This applies anywhere you face information overload.
Define relevance criteria for the task at hand. Score information by relevance. Allocate space by priority. Include highest-value content first. Stop when capacity is reached.
Select documents to include in context. Watch the token budget fill up.
Before adding anything to context, ask: does this directly help answer the question? A support query about a current project does not need the customer's complete history from three years ago. Filter ruthlessly.
AI models pay more attention to content at the beginning and end of context. Put critical information early. Put supplementary details later. If truncation happens, you lose the least important parts.
Assign percentages: 20% for system instructions, 40% for relevant documents, 20% for conversation history, 20% reserved for the response. This prevents any single category from crowding out others.
You uploaded your documentation. You asked a specific question. The AI ignored the relevant section and made something up. Context window management would have prioritized the right information and left out the noise.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
You added the entire knowledge base to every query because you were not sure what the AI might need. Now most queries hit the context limit. The AI truncates important information and hallucinates the rest.
Instead: Use retrieval to find the 3-5 most relevant documents. Include only those. If the AI needs more, let it ask follow-up questions.
You appended the key instruction as an afterthought at the bottom. The AI never saw it because context was truncated. It did exactly what you told it not to do.
Instead: Front-load critical information. System prompts and must-follow rules go at the beginning. Supplementary context comes after.
You gave equal weight to a customer's current question and their support tickets from 2019. The AI spent half its attention on irrelevant history and missed the actual problem.
Instead: Recency and relevance matter. Score context by how directly it relates to the current task. Allocate space accordingly.
You have learned how to control what the AI sees and prioritize what matters. The natural next step is learning to allocate specific token budgets across different context sections.