If you’re building technical docs assistants using RAG pipelines, stop dumping the entire chat transcript into every prompt.
If you are currently appending full history, your pipeline is suffering from context bleed, where the LLM gets confused and mixes up parameters across completely different API endpoints. Plus, you are paying to resend 10,000 historical tokens to answer a 20-word follow-up.
Here is what I did to fix this in my RAG pipeline: I built a Scope-Adaptive Context Gate (SADCG).
Instead of stuffing the prompt, it classifies user intent and filters conversation history by topic scope before RAG retrieval even runs.
What happened after implementing this:
• 21% reduction in total per-chat credit cost
• Up to 40% lower peak context size
• Zero cross-topic hallucination in generated code and API answers
If you are building with RAG pipelines and struggling with context bloat, I wrote a full breakdown of the scope gate architecture, 5 classification modes, and implementation lessons.
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