This past week, I published a sequence of posts on the real failure modes in Retrieval-Augmented Generation systems. Each post focused on a workflow that is often treated as trivial but is responsible for most retrieval instability in production. This article brings everything together. Think of it as a reference hub you can keep open while building or debugging RAG systems.
1. Deep vs Repetitive Work Map A breakdown of which RAG tasks require real engineering judgment and which are purely repetitive but risky to get wrong. This map helps teams avoid over-engineering the wrong areas while under-monitoring the fragile parts.
2. Ingestion Flow Diagram A complete overview of how documents enter your system and where silent drift occurs. Typical failure points include incons…
This past week, I published a sequence of posts on the real failure modes in Retrieval-Augmented Generation systems. Each post focused on a workflow that is often treated as trivial but is responsible for most retrieval instability in production. This article brings everything together. Think of it as a reference hub you can keep open while building or debugging RAG systems.
1. Deep vs Repetitive Work Map A breakdown of which RAG tasks require real engineering judgment and which are purely repetitive but risky to get wrong. This map helps teams avoid over-engineering the wrong areas while under-monitoring the fragile parts.
2. Ingestion Flow Diagram A complete overview of how documents enter your system and where silent drift occurs. Typical failure points include inconsistent exporters, OCR variability, HTML collapse, and partial updates.
3. Retrieval Drift Diagram A model showing how retrieval degrades over time even when the embedding model and vector DB remain constant. Drift patterns include distribution shift, mixed embeddings, format variance, and disappearing neighbors.
4. Chunking Strategy Diagram A guide for stabilizing chunk boundaries across versions of the same document. This clarifies boundary drift, hierarchy flattening, overlapping inconsistencies, and context dilution across formats.
5. RAG Debug Checklist A step-by-step flow to diagnose failures before adjusting models. Checks include ingestion consistency, boundary variance, metadata integrity, embedding drift, retrieval configuration, and evaluation sanity.
6. Chunk Boundary and Metadata Map A detailed map showing how headings, hierarchy, and segmentation produce metadata tags that ultimately determine index quality. This explains why metadata mismatches often cause “random” retrieval failures.
Links to the Original Posts: This hub links back to all six detailed breakdowns from the week. These posts cover the mechanics behind each diagram and include examples from production pipelines. https://dev.to/dowhatmatters/why-40-of-ai-engineering-is-repetitive-glue-work-not-ai-work-497c https://dev.to/dowhatmatters/rag-ingestion-the-hidden-bottleneck-behind-retrieval-failures-1idn https://dev.to/dowhatmatters/chunking-and-segmentation-the-quiet-failure-point-in-retrieval-quality-o8a https://dev.to/dowhatmatters/embedding-drift-the-quiet-killer-of-retrieval-quality-in-rag-systems-4l5m https://dev.to/dowhatmatters/the-boring-debug-checklist-that-fixes-most-rag-failures-201a https://dev.to/dowhatmatters/chunk-boundary-and-metadata-alignment-the-hidden-source-of-rag-instability-1mo6-temp-slug-6787061
Why This Pack Exists RAG failures are rarely caused by embeddings or model choice. They originate upstream, in repetitive steps that are easy to overlook. This workflow pack is designed to make those steps visible, predictable, and diagnosable.