Staging context: how RAG answers follow-ups without re-reading the library

An AI agent that re-scans an entire knowledge base on every follow-up question quickly becomes slow and inefficient. When an agent is conversing with a user, a question like “and what did it say about pricing?” should intuitively build upon what was already referenced in the active thread.

Here is how a staged retrieval architecture keeps agent context sharp, accurate, and cost-efficient.

Staged Retrieval: Cite Before You Scan

In conversational RAG, rather than immediately searching the entire corpus on every prompt, we evaluate context in stages:

1. Stage 1 (Cited Context): Search only across the documents and notes cited in the active conversation thread.
2. Stage 2 (Corpus Fallback): If and only if the query cannot be resolved with high confidence in the existing context, escalate to a full library search.

This tiered approach provides two major advantages:

• Zero Latency on Follow-ups: Answering questions regarding previously cited materials avoids repetitive multi-thousand-document scans.
• Contextual Coherence: The agent prioritizes consistency with the narrative established in the current session.

High-Level Algorithm

function RetrieveRelevantContext(query, activeSession):
    # Step 1: Check active session citations first
    citedDocumentIds = activeSession.getCitedDocuments()
    
    if citedDocumentIds.isNotEmpty():
        localMatch = SearchEngine.queryWithinScope(query, scope=citedDocumentIds)
        if localMatch.confidenceScore >= HIGH_CONFIDENCE_THRESHOLD:
            return localMatch
            
    # Step 2: Fall back to full library search
    globalResults = SearchEngine.queryFullCorpus(
        query, 
        modes=[EXACT_MATCH, FUZZY_PREFIX]
    )
    
    # Step 3: Register newly discovered citations
    activeSession.registerCitations(globalResults.documentIds)
    
    return globalResults

Rolling Memory Windows & Summarization

To maintain infinite conversation length without exceeding prompt token budgets:

• Active Raw Turns: The most recent dialogue turns are supplied verbatim to preserve precise conversational nuance.
• Incremental Summarization: Older dialogue history is asynchronously compressed into structured background summaries.

This guarantees that an extended 100-turn agent session consumes predictable token volume while retaining critical prior decisions and factual grounding.