Domain Driven Rag

Retrieval augmented generation can help reduce LLM hallucination. Applying high-quality metadata and distributing ownership of documents and prompts to domain experts can further increase accuracy. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/domain-driven-rag/).

What Happened

InfoQ Homepage Articles Domain-Driven RAG: Building Accurate Enterprise Knowledge Systems through Distributed Ownership

Domain-Driven RAG: Building Accurate Enterprise Knowledge Systems through Distributed Ownership

Modular Retrieval Augmented Generation (RAG) applications enhance accuracy and relevancy by assigning ownership to dedicated domain experts.

Metadata should be leveraged to intelligently route queries to the most appropriate RAG application, whether through auto-selection, manual choice, or comprehensive search.

Domain experts must own both content curation and system prompt engineering to ensure technical accuracy in specialized areas.

Technical diagrams should be converted into textual representations to enrich RAG systems with architectural knowledge.

RAG capabilities should be built into complete tools that integrate with existing workflows, not offered as standalone AI interfaces.

As a leading banking tech vendor with over 30 years in the industry, we have developed an extensive proprietary codebase and expanded through strategic acquisitions. Over the decades, we've positioned ourselves as innovators, yet the rapid pace of innovation has brought challenges in maintaining consistent and up-to-date documentation across our vast product lineup.

While some areas of our codebase have solid, well-managed documentation, others are unclear or outdated, making

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.