RAG Architecture: Grounded, Not Hallucinated
How we use retrieval-augmented generation to ensure AI responses are traceable to source material.
The Hallucination Problem
Large language models generate plausible-sounding text. That's the feature and the bug. When asked about your specific infrastructure, pricing, or processes, a general-purpose LLM will confidently make things up.
CloudPresto's AI workflows use Retrieval-Augmented Generation (RAG). The AI retrieves relevant context from a curated knowledge base before generating any response. Every answer is grounded in actual source material.
How RAG Works
Query Understanding
The user's question or input is analyzed to understand intent and extract key concepts. What are they actually asking? What domain knowledge is needed?
Knowledge Retrieval
Relevant documents, runbooks, pricing data, and service descriptions are retrieved from the curated knowledge base. Semantic search finds contextually relevant content, not just keyword matches.
Grounded Generation
The AI generates a response using only the retrieved context. If the knowledge base doesn't contain the answer, the AI says so. It doesn't fabricate one.
Source Attribution
Every claim in the response is linked to its source document. Reviewers (and auditors) can verify any statement by checking the source material.
Knowledge Base Curation
RAG is only as good as its knowledge base. Garbage in, garbage out, even with retrieval. CloudPresto maintains a curated knowledge base with:
- Versioned content: every document has a last-reviewed date and owner
- Regular audits: stale content flagged and updated or removed
- Domain separation: pricing, technical specs, operational procedures, and customer-facing content in separate indexes
- Access controls: internal knowledge never surfaces in customer-facing workflows