finance_ops · energy · workflow
From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2
Building RAG systems that handle heterogeneous data formats—structured tables, unstructured text, and images—requires distinct retrieval and processing strategies per data type, which a single uniform approach cannot address, especially since LLMs perform poorly on raw tabular data.
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 query submitted
A user query arrives and initiates the retrieval and processing workflow.
Tools used
Amazon BedrockClaude HaikuClaude Sonnet 3.5Claude Sonnet 3Amazon Titan Multimodal EmbeddingsAmazon Titan Embedding Text v2OpenSearchLlama IndexLangChainBedrock Converse API
Outcome
By employing intent detection routers, LLM code generation, and multimodal embeddings, the GenAIIC team built intelligent RAG systems spanning oil and gas, financial, industrial, and ecommerce use cases that deliver coherent responses across heterogeneous data sources.
Results
Cost replaced92%
Grounding & classification
Source type: technical build writeup
29 fields verified against source quotes.
code generationcomputer visiondata extractionragknowledge basemetric backedsource backedtools describedworkflow describedecommerceenergyfinancial servicesmanufacturingtechnical build writeupecommerce opsfinance opsit supportextract classify routerag answering