Designing Retrieval Factories for Enterprise Knowledge

Most RAG failures trace back to ingestion. Before a single prompt gets processed, before embeddings are generated, before retrieval happens—the data pipeline must be bulletproof. The ingestion contract defines the quality gates, security checks, and governance rules that every data source must pass before entering your knowledge base. This isn't about being overly cautious; it's about preventing downstream failures that are expensive to fix. When sensitive information leaks through RAG systems, when outdated content produces incorrect answers, or when policy violations trigger compliance issues, the root cause is almost always a weak ingestion process. We've developed a rigorous checklist that we apply to every repository, database, and API before it touches an embedding model. This contract-based approach transforms data ingestion from an ad-hoc process into a repeatable, auditable system. The upfront investment in robust ingestion pays for itself many times over by preventing incidents, reducing manual review, and enabling confident scaling.

The machine learning community has spent years perfecting feature stores for numeric data—systems that version, catalog, and monitor features to enable reproducible experiments and production deployments. It's time to apply the same rigor to text embeddings. Treating embeddings like first-class features means establishing canonical schemas that describe not just the vector itself, but its complete lineage: source document, chunking policy, embedding model version, and content hash. This metadata becomes critical when debugging retrieval issues, comparing model performance, or satisfying audit requirements. Feature stores for text enable teams to version embeddings alongside the models that use them, track drift as source data evolves, and A/B test new embedding strategies without disrupting production. The investment in this infrastructure pays dividends as RAG systems scale, making it possible to answer questions like 'Why did retrieval quality drop last week?' or 'Which embedding model performs best for our use case?' with data rather than guesswork.

Production RAG systems generate thousands of queries daily, each one a potential source of insight about system performance, user needs, and improvement opportunities. But without proper observability, teams are flying blind. When retrieval quality degrades, when latency spikes, or when users report incorrect answers, the lack of visibility makes diagnosis slow and expensive. The solution is a comprehensive observability stack that treats RAG as a distributed system requiring the same monitoring rigor as any critical infrastructure. This means instrumenting every layer: tracking recall and precision at the retrieval stage, measuring answer accuracy at the generation stage, monitoring latency across the entire pipeline, and capturing rejection reasons when systems decline to answer. We've developed what we call the 'RAG SLO stack'—a set of service level objectives, metrics, and tooling that enable teams to understand system behavior in real-time and make data-driven improvements. This observability infrastructure doesn't just help with debugging; it enables continuous optimization, A/B testing, and proactive issue detection that prevents problems before users notice them.