The Naive RAG Failure Pattern
Most developers begin by embedding documents into a vector database, performing cosine similarity with the user's latest query, and feeding the top chunks into an LLM context. While this works for single-question demos, it breaks down quickly in real conversations.
When a user asks: 'What was their revenue last year?' and then follows up with 'And how does that compare to the previous quarter?', raw similarity search completely misses the pronoun reference ('that') and retrieves irrelevant documents.
Contextual Query Rewriting and Verification
To solve multi-turn conversational degradation, we implemented an autonomous pre-flight query rewriting step. Before hitting the vector index, an ultra-fast small model (e.g. Claude 3.5 Haiku or DeepSeek Chat) rewrites the conversational history into a standalone disambiguated search statement.
Post-retrieval, chunks are passed through a cross-encoder reranker to score semantic relevance before being injected into the final system prompt.
// Multi-turn conversational query disambiguation
async function rewriteContextualQuery(history: Message[], currentPrompt: string) {
const systemPrompt = "Given conversation history, formulate a standalone search query.";
const rewritten = await fastLLM.generate({
system: systemPrompt,
messages: [...history, { role: "user", content: currentPrompt }]
});
return rewritten.trim();
}Production Reliability & Hallucination Guardrails
By strictly validating generated outputs against Zod schemas and forcing citations to map to known document IDs, enterprise clients can safely automate mission-critical customer and internal intelligence queries without human oversight.