4 articles
Upgrading an embedding model is more than swapping a model name. This post breaks down production migration strategies—dual writes, background re-embedding, blue-green vector indexes, atomic alias switching, rollback windows, and incremental updates—to avoid recall drift and service disruption.
Tokenizer changes can silently shift token counts, truncation boundaries, and costs for the same prompt. This article provides practical solutions including compatibility fingerprints, golden corpus replay, dual counting, and budget gates to help teams identify risks before model upgrades and safely roll out changes.
Multi-model provider integration goes beyond a unified SDK. This article systematically covers production governance for LLM adapter layers—from capability matrices, parameter normalization, and response contracts to streaming event adaptation, tool call lifecycles, and migration replay testing—helping teams reduce model migration costs and production risks.
A deep dive into production-grade governance for LLM chat templates, covering template version management, special token handling, precise token counting, tool call rendering, and replay testing to prevent quality regressions and cost overruns during model switches.