Retrieval-Augmented Generation Systems

Advises on optimal document chunking, splitting, and preprocessing strategies for RAG pipelines to maximize retrieval accuracy across varied content types like PDFs, code, and tables.

Guides implementation of hybrid search combining dense vector retrieval with sparse keyword methods like BM25, including fusion techniques and weighting strategies for better RAG accuracy.

Designs robust pipelines for ingesting, cleaning, updating, and syncing heterogeneous source data such as PDFs, wikis, and databases into a RAG-ready knowledge base.

Expert guidance on designing end-to-end retrieval-augmented generation architectures, from data ingestion to retrieval and generation layers, tailored to your use case and scale.

Systematically diagnoses why RAG systems return wrong, irrelevant, or hallucinated answers by isolating faults across retrieval, chunking, embedding, and generation stages.

Builds and interprets evaluation frameworks for RAG retrieval quality, including recall, precision, groundedness, and answer relevance metrics, to systematically improve system accuracy.

Specialized help tuning vector database performance, indexing algorithms, and query parameters to speed up retrieval and cut infrastructure costs in RAG systems.