Lesson 660 of 1596
Context Window Budgeting: What to Include, What to Cut
Long context windows tempt teams to dump everything in. Smart prompting means choosing what context actually helps — and ruthlessly cutting what doesn't.
Creators · Prompting · ~24 min read
The premise
More context isn't always better; performance can degrade with irrelevant context, and cost always increases.
What AI does well here
- Curate context relevance — included items should each earn their place
- Test 'lost in the middle' — long contexts can ignore middle content
- Position critical instructions at start AND end (recency + primacy effects)
- Measure quality at different context lengths to find the sweet spot
What AI cannot do
- Solve all problems by adding more context
- Substitute context for retrieval quality (bad RAG doesn't get fixed by more chunks)
- Eliminate the cost-per-token reality
Key terms in this lesson
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
Tutor
Curious about “Context Window Budgeting: What to Include, What to Cut”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 40 min
Multi-Turn Conversation Design: Memory, State, and Sessions
Single-turn prompts are easy. Multi-turn conversations require thinking about state, summary, and what to surface back to the model — design choices that determine whether the conversation stays coherent.
Builders · 17 min
Context Window Discipline: What Fits in AI's Memory
Pasting a 50-page document plus your question often gets a worse answer than pasting just the relevant 2 pages.
Creators · 40 min
System Prompt Architecture: Design, Layering, and Conflict Policy
Production system prompts are layered constraint stacks. Design capability, safety, brand voice, examples, and instruction precedence together so the model knows what wins when messages disagree.