Long Context

Providers charge more for long contexts, and one of the core reasons is the cost of the KV cache growing linearly with context length.

Before ChatGPT came out, the roadmap already listed research directions such as Coding Agents, Tool Use, long context, RL, automated feedback, and Agent safety.

When processing large codebases or long documents, strategies such as retrieval-augmented generation (RAG) are still often used together with long context.

Long context is suitable for batch operation scenarios such as whole-repository refactoring, complete transcript analysis and cross-document deduplication, but extracted claims still need verification as a second pass.

Long-context calls need budgets set per team, with input token histograms recorded weekly, where peaks correspond to bad prompts or missing RAG.

A common hybrid architecture in practice is: first use long context to generate a document map or summary index, then use RAG to locate specific fragments according to the question, and finally hand the retrieval results together with the map to the model for synthesis, requiring cited output (chunk IDs).

Buy retrieval first, then buy tokens; on every enterprise task the article's author measured, structure beat volume.

The new KPI of the long-context race is 'usable, easy to use, and not losing information', rather than simply handling longer text.

For continual learning that does not involve weight updates (long context, memory files, vector stores), the risk is lower; text memories are readable, and developers can read and delete content that describes evading the monitor.

The approach of directly filling the context carries higher token consumption, but in return offers determinism and debuggability, as developers know exactly what the model has seen.

Including large amounts of conversation history or background documents (long context) in each request causes costs to accumulate in a non-linear manner.

Processing 41 documents simultaneously requires the model to have strong long-context comprehension capabilities and cross-file cross-validation capabilities.

Whether a model can accurately locate key information in an ultra-long context and avoid the lost-in-the-middle phenomenon is a decisive factor in determining coding performance.