Muaz Ashraf has built over 20 production RAG systems for enterprise clients across multiple countries, addressing common failure modes encountered when scaling from demos to real-world usage. Key architectural patterns include self-correction loops to reduce hallucinations, incremental re-indexing with content hashing to keep embeddings fresh and reduce costs, hybrid retrieval combining dense vector and sparse keyword search with reranking to improve accuracy especially in domains like healthcare, multimodal embeddings to handle visual content in documents, and automated evaluation pipelines with golden datasets to monitor system performance. These patterns have demonstrably improved accuracy from ~70% to over 90%, reduced hallucinations to single digits, saved 70% on embedding API costs, and restored user trust by ensuring up-to-date retrieval.
Use Case
Opening the operator briefing
Pulling the full operator breakdown, tooling context, and verification notes.
