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Large language models have static, often outdated training data, are expensive and slow to recalibrate, and are prone to producing fabricated answers, making them unreliable for domain-specific or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.
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.
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.
Frequently asked questions
What did this team achieve with this AI workflow?
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 ma…
What tools did this team use?
Coveo, Lucidworks, GPT 3.5 turbo.
What results were reported?
Time to resolution (field service case): 15 minutes; case open duration before RAG resolution: tow month (source-reported, not independently verified).
What failed first in this deployment?
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-…
How is this customer support AI workflow structured?
Rep submits query → Retrieve relevant documents → LLM generates grounded response → Rep applies resolution.