RAG Systems
Retrieval architectures.
Sub-Track Scope & Core Architecture
The RAG Systems track delivers specialized engineering blueprints, verification protocols, and reference implementations tailored for mission-critical deployments.
Curriculum Topics (8)
Document Ingestion Pipelines & Layout Parsing
Architecting resilient ingestion pipelines: PDF layout extraction, OCR noise compensation, table preservation, and metadata enrichment.
RAG Staged Improvement & Failure Mode Maturity Progression
A 5-stage evolutionary maturity model for diagnosing accuracy plateaus and systematically eliminating failure modes from naive prototypes to verified attribution.
Hybrid Search: Dense Vector & Sparse BM25 Fusion
Layered retrieval architectures: overcoming vector search blind spots with sparse BM25 fusion, Reciprocal Rank Fusion (RRF), cross-encoders, and verifiable provenance.
Document Structure Preservation & Format Trust Layers
Preserving Markdown and Semantic HTML markup to prevent plain-text tabular collapse, heading scope erasure, and arbitrary chunk boundary cuts.
Markdown-Native Authoring Strategy for AI Knowledge Bases
Eliminating conversion loss upstream through Git-backed Markdown-native and semantic HTML knowledge authoring, AST-level chunking, and dual-use documentation.
Script-per-Document-Type Ingestion Strategy
Building purpose-built, maintainable transformation scripts for heterogeneous enterprise document formats to prevent silent structural degradation.
Chunking & Document Segmentation Strategy
Optimizing chunk boundaries, structure-aware AST segmentation, overlap calibration, and query-pattern-driven token sizing in RAG pipelines.
RAG Evaluation Benchmarks: Precision, Recall & RAGAS
Automating quantitative RAG evaluation using Context Precision, Context Recall, Faithfulness, and Answer Relevance metrics.
Copy this prompt to evaluate trade-offs with your AI coding partner.
Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.