Production RAG Architectures & Vector Database Engineering
Target audience: Backend engineers, data engineers, and enterprise architects.
Applicable duration: 60 mins · 120 mins
While basic RAG prototypes can be spun up in an afternoon, enterprise RAG systems frequently suffer in production: irrelevant chunks dilute model attention, tables and nested data are mangled during ingestion, and retrieval precision degrades as knowledge bases grow. Building production-grade RAG requires advanced engineering disciplines.
In this deep-dive 1:1 session, you will explore advanced retrieval architectures: semantic document parsing, hybrid search (combining BM25 keyword matching with dense vector similarity), cross-encoder reranking, and citation tracking to eliminate hallucinations entirely.
What you will learn
- Advanced Document Chunking Implementing recursive, semantic boundary, and sliding-window chunking strategies.
- Vector Store Optimization Administering and tuning indexing algorithms (HNSW vs. IVF) across Pinecone, Qdrant, and pgvector.
- Hybrid Search Implementation Combining lexical keyword matching (BM25) with dense semantic embeddings to maximize retrieval recall.
- Cross-Encoder Reranking Deploying reranker models (Cohere Rerank / BGE) to prioritize the most relevant factual passages.
- Strict Source Attribution & Grounding Designing prompt templates that force models to quote verifiable source documents with page citations.
Qualifications you will acquire
- Advanced RAG Pipeline Engineering Mastery: Demonstrated ability to build enterprise-scale retrieval systems that deliver high accuracy on complex documents.
- Vector Database Administration Competency: Proven skill in managing, scaling, and indexing high-dimensional vector databases.
- Hallucination Mitigation Qualification: Expertise in enforcing factual grounding and source provenance in enterprise data queries.
Key features
- Live Pipeline Construction: Build an advanced RAG system live using your own complex enterprise PDFs and documents.
- Retrieval Benchmarking: Measure precision and recall trade-offs across different chunking sizes and embedding models.
- Production RAG Code Repository: Take away modular Python source code complete with hybrid search and reranking helpers.
