customer_support · saas · workflow
Enhancing LLM accuracy with Coveo Passage Retrieval on Amazon Bedrock
Enterprises adopting LLMs face the challenge that without reliable data foundations these models can generate misleading or inaccurate responses, reducing user trust and organizational credibility. In RAG systems, extracting the most relevant, precise information from enterprise data sources is the most complex component.
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 · User submits natural language query
Amazon Bedrock Agents receives a natural language query and orchestrates the retrieval process to deliver grounded, contextually relevant insights.
Tools used
Coveo AI-Relevance PlatformAmazon Bedrock AgentsAWS LambdaAmazon CloudWatchAWS CloudFormationClaude 3 Haiku v1
Outcome
By integrating Coveo's Passage Retrieval API with Amazon Bedrock Agents, organizations can develop AI applications that provide validated responses based on enterprise content, helping reduce inaccuracies while delivering real-time, secure responses.
Grounding & classification
Source type: technical build writeup
20 fields verified against source quotes, 2 dropped as unverifiable.
ai agententerprise searchknowledge searchragknowledge basevendor confirmedworkflow describedsoftwareaccuracy improvementtechnical build writeupback office opscustomer supportrag answering