Enterprises Adopt SLMs With RAG Architecture
Enterprises are moving from large LLMs to small language models (SLMs) paired with retrieval-augmented generation (RAG) to reduce operational cost, improve latency and increase auditability in production systems. The article outlines a modular, agent-based architecture using per-agent RAG indexes and protocols like Agent2Agent (A2A) and Agent Name Service (ANS); benchmarks cited show roughly a 5 percentage-point QA accuracy gain. This approach aims to deliver predictable costs, verifiable outputs and governance hooks for regulated industries.
Key Points
- 1Adopt SLMs with RAG: compact, domain-specific models run efficiently on CPUs or modest GPUs.
- 2Use RAG to ground outputs and improve accuracy—benchmarks show ~5 percentage point QA improvement.
- 3Design modular agent services with A2A and ANS to enforce interoperability, auditability, and governance.
Scoring Rationale
Practical, widely applicable architecture and actionable guidance, limited by lack of new empirical evidence and formal benchmarks.
Sources
Public references used for this report.
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