Why grounding matters
Large language models alone will confidently generate incorrect answers. Retrieval-augmented generation grounds responses in your organization's actual, current documents — dramatically improving factual accuracy and giving every answer a traceable source.
Chunking and indexing strategy
How documents are chunked before indexing directly affects retrieval quality. Chunk size, overlap, and metadata enrichment need to be tuned to the structure of your content — policy documents behave differently than technical specifications.
Semantic ranking
Combining vector similarity search with semantic ranking improves relevance beyond either technique alone, particularly for enterprise content with domain-specific terminology.
Citations as a design requirement
Every generated answer should be traceable to its source document. Building citation display into the application from the outset — not as an afterthought — is what makes an enterprise AI system trustworthy enough for regulated environments.
// Simplified RAG query pattern
const results = await searchClient.search(query, {
queryType: "semantic",
vectorQueries: [{ vector: embedding, fields: "contentVector" }],
select: ["title", "content", "sourceUrl"]
});