A concise guide explaining why DIY retrieval-augmented generation (RAG) systems can often break when scaled, and how a production‑grade, governed RAG‑as‑a‑Service solution simplifies ingestion, retrieval, evaluation and security to deliver reliable enterprise AI.
This comprehensive whitepaper covers:
- DIY RAG prototypes work early, but fail as complexity, data sources and use cases grow
- Enterprise AI requires more than just a good model, but also deterministic retrieval, source attribution and governed access
- Production‑grade RAG demands hybrid retrieval, built‑in evaluation and full observability to maintain trust at scale
- Maintenance of DIY pipelines creates compounding operational costs across ingestion, re‑embedding and ranking
- RAG‑as‑a‑Service centralizes ingestion, retrieval and governance—enabling faster, safer, multi‑use‑case deployment
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