> forward_deployed
Optimizing the RAG Pipeline
Getting RAG working is the first half; making it accurate, fast, and affordable under real load is the half that decides whether the customer renews. My optimization loop is measured, not vibes: every technique below earned its place by moving an evaluation metric.
> what this looks like in practice
▸Reranking after retrieval: the first-pass retriever recalls wide, a reranker orders for precision, and the context window only sees what survives both. Implemented in the AI-Infused QE Platform's retrieval path.
▸Context compression before generation, so long documents contribute their relevant spans instead of their bulk. This is a quality technique and a cost technique at once.
▸Query transformation and rerouting at the front door: malformed or underspecified queries get rewritten, and low-complexity queries route to lighter models before touching the expensive path.
▸Evaluation loops close the cycle: retrieval precision measured against a held-out set in the conversational RAG pilot (response relevance up 30-40%), and RAGAS plus Acceptance Criteria Coverage and Test Design Coverage running continuously in the QE platform.
> where it shows up
The AI-Infused QE Platform (the ~15% to under 5% hallucination drop came from this loop, not from one silver bullet) and the enterprise conversational RAG pipeline.