AI Data Engineering and RAG Practitioner

Foundational RAG concepts for AI data engineers: architecture, limits and source governance. Covers source inventory, data ownership, freshness, ACLs, parsing common formats (PDF, HTML, Markdown, CSV) and metadata design for citations and filtering.

Describes RAG architecture, when RAG is inappropriate, and core considerations (key concepts and takeaways).

Details source inventory, data ownership, freshness controls and ACLs to support reliable RAG pipelines.

Covers practical parsing approaches for PDFs, HTML, Markdown, CSV and structured records and their impact on indexing.

Outlines metadata design principles to enable accurate citations and effective filtering in RAG retrievals.

Chunking and embedding techniques: chunking strategies (fixed, semantic, recursive, document-aware), embedding model selection including multilingual options, deduplication and normalization, and incremental indexing with re-index triggers.

Compares chunking strategies suited to RAG pipelines and explains trade-offs by document structure.

Presents criteria for choosing embedding models and multilingual considerations for robust vector search.

Describes deduplication, normalization and content hashing methods to reduce redundancy and inconsistencies in indexes.

Explains incremental indexing and re-index triggers to keep data relevant without rebuilding the entire corpus.

Vector and hybrid search: fundamentals (cosine, dot product, HNSW, IVF), using pgvector for small-to-medium RAG, Qdrant for dedicated vector search with payload filtering, and hybrid BM25+vector+reranking strategies.

Introduces vector search principles (similarity measures, approximate indexes like HNSW/IVF) and their impact on latency/quality.

Explains integrating pgvector into Postgres as a practical option for small-to-medium RAG setups.

Describes Qdrant as a dedicated engine including payload filtering and appropriate usage scenarios.

Outlines hybrid architectures combining BM25, vector search and reranking stages to boost precision and recall.

Grounded answers and citations: prompt template patterns for grounded responses, source attribution UX, document-level access control, and production operations (refresh, jobs, monitoring, rollback).

Describes prompt template patterns intended to produce responses grounded in retrieved passages and associated best practices.

Covers the UX of presenting citations and attributing sources to preserve traceability of generated answers.

Specifies document-level access control mechanisms and permissions to enforce access rules and confidentiality.

Addresses production operational concerns: data refresh, indexing jobs, pipeline monitoring and rollback strategies.