AI BriefingRAG Engineering
Key point
Model racing techniques cut RAG query rewriting latency by more than 50% while securing system resilience.
Details
RAG (Retrieval-Augmented Generation) is essential for improving the accuracy of LLM responses, but the Query Rewriting stage, which converts conversational context into a refined query, can cause serious latency. Existing systems relied on external LLMs, causing this stage to become a bottleneck that accounted for over 80% of total RAG latency.
To address this, a Model Racing architecture was introduced, running multiple models in parallel and adopting whichever model produces a valid response first.
- Parallel Execution: Queries are sent simultaneously to multiple models, including the self-hosted Qwen 3-4B and 3-30B-A3B models.
- Fallback Mechanism: If no response arrives within 1 second, the user's original message is used as-is so the flow of conversation is not interrupted.
Through these design changes, median RAG latency was reduced by more than 50%, from 326ms to 155ms. In addition, since self-hosted models can respond immediately even during outages at external LLM providers, the system's resilience has been significantly improved.
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