customer_support · workflow
Demystifying RAG: Enhancing LLMs with Real-Time Knowledge Retrieval
Large language models have static, often outdated training data, are expensive and slow to recalibrate, and are prone to producing fabricated answers, making them unreliable for domain-specific or current-knowledge queries.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Rep submits query
A call center or field service representative submits a question or task about a customer issue.
Tools used
CoveoLucidworksGPT 3.5 turbo
Outcome
RAG implementations progressed from proof-of-concept to production roll-out, with a field service case illustrating how a previously stalled truck repair was resolved in 15 minutes after the RAG solution surfaced a matching historical case.
What failed first
Databricks' Data Intelligence Platform lacks support for conditional filtering and boosting, causing it to underperform in relevance calculation during retrieval and making it insufficient on its own for a production-grade RAG implementation.
Results
Time saved15 minutes
Volumetow month
Grounding & classification
Source type: technical build writeup
22 fields verified against source quotes, 1 dropped as unverifiable.
chatbotknowledge searchragknowledge basefailure mode describedmetric backedproduction runtime claimedtools describedworkflow describedfinancial servicesinsuranceemployee productivityresolution time reductiontechnical build writeupcall center aicustomer supportfield servicerag answering