Tag: RAG_Architecture

Events with RAG(LLM)

Posted on by lechuck park

Step 1: Event Detection & Ingestion

This initial stage focuses on capturing system anomalies through real-time monitoring, collecting necessary logs, and extracting essential metadata to understand the context of the event.

Step 2: RCA: Root Cause Analysis

It identifies the fundamental issue behind the surface-level symptoms by utilizing correlation analysis, distributed tracing, root cause drill-down, and infrastructure topology analysis.

Step 3: Query Formulation for RAG

The system translates the RCA findings into an optimized search prompt through query reformulation, entity extraction, and intent classification to fetch the most accurate solutions.

Step 4: Retrieval

It searches for the most relevant technical documents or past incident records from a Vector Database, leveraging hybrid search, chunking strategies, and document re-ranking techniques.

Step 5: Generation via LLM

The LLM generates an actionable troubleshooting guide by combining prompt engineering and context injection, strictly mitigating any AI hallucinations.

Step 6: Action & Knowledge Update

Finally, after the issue is resolved, the system automatically updates its knowledge base with post-mortem reports, ensuring a continuous feedback loop through an automated LLMOps pipeline.

Summary

  1. Event Detection & Root Cause Analysis: When a system incident occurs, it is captured in real-time, and the system deeply traces the actual root cause rather than just addressing surface-level symptoms.
  2. Knowledge Retrieval & Solution Generation: The analyzed root cause is transformed into a RAG-optimized query to retrieve the best reference documents from the internal knowledge base, allowing the LLM to generate an immediately actionable troubleshooting guide.
  3. Knowledge Capitalization & Virtuous Cycle: Once the issue is resolved, a post-mortem report is generated and automatically fed back into the knowledge base, creating a continuously evolving and automated pipeline.

#AIOps #RAG_Architecture #RootCauseAnalysis #LLMOps #IncidentManagement #TroubleshootingAutomation #VectorDatabase

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