External knowledge claims include appropriate attribution cues

RAG pipelines that retrieve document chunks but strip source metadata before passing them to the model make it impossible for the AI to correctly attribute its answers. Users reading the response have no way to verify claims, locate original documents, or identify when the model has drawn on training data instead of retrieved content. For regulated industries — legal, healthcare, financial — unattributed claims create liability and compliance risk. OWASP LLM09 classifies unattributed AI-generated content as a misinformation risk. Without attribution cues, the inference-contract between the retrieval system and the user is broken.

Pass source metadata alongside retrieved chunk content and instruct the model to reference it:

const contextBlock = chunks.map(c =>
  `[Source: ${c.documentTitle}, p.${c.pageNumber}]\n${c.content}`
).join('\n\n')

const systemPrompt = `
Answer using only the provided context. When citing information,
reference the source document by name: "According to [Document Name]..."
`

For non-RAG applications, instruct the model to qualify training-data claims with "Based on my training data" so users can distinguish model knowledge from retrieved evidence.