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Production RAG Pipeline for Support Automation

AI Engineer
RAG Python Generative AI Monitoring Backend
Stack: Python, RAG, LLMs

Context

COMPUMAX needed to automate repetitive support workflows that were consuming significant agent time. The goal was a production-grade retrieval-augmented generation pipeline that could handle real support queries with high reliability — not a prototype, but a system that could be trusted in daily operations.

Approach

End-to-End Pipeline Design

Designed and built the full RAG pipeline from prototype to deployment:

  • Document ingestion and chunking for the support knowledge base
  • Retrieval layer with relevance ranking
  • LLM-powered response generation grounded in retrieved context
  • Backend services in Python handling the full request lifecycle

Retrieval Quality Evaluation

Implemented evaluation workflows to measure and continuously improve retrieval quality. This included automated checks for retrieval relevance and answer faithfulness, creating a feedback loop for ongoing improvement rather than a one-time deployment.

Monitoring & Observability

Added monitoring and observability practices to support safe releases and ongoing maintenance. The system tracks key quality indicators so regressions can be caught before they affect users.

Business Alignment

Worked closely with stakeholders to align the solution with business requirements and operational constraints — ensuring the system fit into existing support workflows rather than requiring the team to adapt to the tool.

Technical Details

  • Stack: Python, RAG pipeline architecture, LLMs
  • Focus areas: Retrieval quality, evaluation workflows, monitoring, backend services
  • Deployment: Production environment with observability and continuous improvement

Outcome

Delivered a production-grade RAG system that automated support workflows with high reliability, reducing manual effort while maintaining quality standards for customer-facing responses.