Avoid context bloat by treating the context window as a budget and designing your app to reuse stable information while rotating in only what’s relevant for the current step. Even with large context options, dumping full chat history, entire documents, and long code files will slow requests, increase costs, and often reduce quality because critical constraints get buried. The most effective approach is progressive context: start with a compact state summary, add retrieved evidence, and fetch additional details only when needed.
A practical “anti-bloat” toolkit:
State object: maintain a short JSON summary of the task: goal, constraints, decisions, and TODOs. Re-inject that each turn instead of appending the entire transcript.
Conversation compaction: periodically summarize older turns into a short bullet list and drop the raw text from the prompt.
Retrieval-first: don’t paste docs; retrieve top-k chunks.
Context trimming: remove boilerplate from user content (signatures, repeated headers).
Bounded tool use: let the model request specific files/sections instead of giving it the whole repo.
For example, in a debugging session, you don’t need the entire log history—usually the failing test name, stack trace, and 1–3 relevant files are enough to get started.
RAG is the cleanest solution: store docs and knowledge in Milvus or Zilliz Cloud, retrieve what’s relevant, and keep your prompt stable. If you need multi-turn conversations, keep the user intent and key constraints in the state object and let retrieval supply the facts. This prevents “token creep” where every turn gets larger. Context bloat is rarely solved by “bigger context”; it’s solved by better information architecture: concise state + precise retrieval + strict output contracts.