One million tokens: feed it the whole thing.
A ~1M-token window means you can stop summarizing-to-fit and just hand DeepSeek the entire document, contract, or repository. This is how to use that well — and where it genuinely changes what is possible.
01What the big window unlocks
Whole-document Q&A without chunking. Cross-referencing many files at once. Codebase-wide questions ("where is X used and what would break if I change it?"). Long-transcript analysis. The job shifts from fitting context to structuring it.
02How to structure a giant prompt
- Put the instruction first and last — models attend most to the start and end of a long input.
- Label each source clearly (FILE: …, SECTION: …) so the model can cite where an answer came from.
- Ask for citations to the labels — it keeps the answer grounded in your text, not its training.
- For codebases, include a short file tree so it understands the structure before the contents.
The "what would break" review
Paste the relevant files, then: "I want to change [function]. List every place it is used, what depends on its current behavior, and the tests I should add before touching it." This is where long context pays for itself.
A bigger window is not infinite attention — quality can dip if you bury the key material in the middle of a huge dump. Keep the signal high, label sources, and ask for citations so you can verify.
Frequently asked
DeepSeek — your questions, answered
How big is DeepSeek's context window?
Roughly one million tokens on both V4 Flash and Pro — large enough to load entire documents or codebases in a single prompt.
How do I prompt with very long inputs?
Put the instruction at the start and end, label every source, ask for citations to those labels, and include a file tree for codebases.
Can DeepSeek analyze a whole codebase?
Yes — within the context limit you can paste many files and ask codebase-wide questions like where a function is used and what would break if you changed it.
Does a bigger context window mean better answers?
Not automatically — attention can dip if key material is buried in a huge dump. Keep inputs high-signal, label sources, and verify with citations.
Keep going
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