Windows got bigger, problems changed too
How to code efficiently inside 1M+
Dumping the entire codebase often backfires. Learn selective loading, cache breakpoints and structured indexing to turn large windows into real advantage.
Real cost and benefit of large windows
Larger context brings higher cost and lower signal-to-noise. Effective utilization is often only 10-30%. The key is not stuffing more, but letting the model see only the precise information it truly needs each time.
Segmentation and subtask strategy
Break long tasks into small steps and supply only the necessary context at each step. More precise than stuffing the entire codebase at once.
Practical example
For refactoring, first cache the architecture overview, then add only the files to change. Explicitly exclude unrelated files.
Effective cache prefix design
Turn system prompts, core architecture descriptions, and common tool definitions into stable prefixes. In long sessions, hits on these can save 70-90% of input cost.
Selective context loading
Prefer repo map + relevant file subset + explicit cache prefixes. Models reason better in clean context.
Cost comparison and measurements
Compare token volume and cost: full context vs selective vs with cache. Use the QCode dashboard to observe actual consumption and iterate your strategy.
When retrieval beats stuffing the window
Before stuffing everything into the window, consider fetching needed info via search or MCP tools. This also mitigates mid-context recall degradation.
Long context practices on QCode
Access large-window models through the same key and observe actual token consumption in the dashboard to quickly iterate your context strategy.
Long context FAQ
Should I put the entire repo in?
No. Prefer repo map + relevant file subset + explicit cache prefixes. Models reason better in clean context.
How to design cache prefixes most effectively?
Make system prompts, core architecture descriptions, and common tool definitions into stable prefixes. In long sessions these can save 70-90% of input cost when hit.
Does switching models mid-way lose context?
The client maintains message history. History is preserved when switching, but the new model may interpret previous tokens differently; summarize when necessary.
What special help does QCode provide?
A single key lets you use models with different window sizes simultaneously. The dashboard splits usage by model, making it easy to compare strategy effects.
Use large windows well
Sign up for QCode and test long-context performance across models.