RAG SYSTEM ARCHITECTURE · 2025

Document Chatbot
RAG Planner

Decision support for designing retrieval architectures and estimating ingestion plus operational costs before committing to implementation.

☁️

Stack A

Azure Native

95%

🧩

Stack B

Best-of-Breed

97%

🦙

Stack C

LlamaCloud Managed

94%

67%

Failure Rate Cut

Contextual + Hybrid + Reranking

93-97%

Peak Accuracy

Stack B on structured corpora

3.5x

Recall Gain

Hybrid vs vector-only search

Hours

Time to Production

Stack C (LlamaCloud managed)

The 3-Technique Foundation

Regardless of stack, these three techniques deliver a 67% reduction in retrieval failures.

1Hybrid Search
2Contextual Retrieval
3Reranking

Image & Diagram Handling

A · OCR First

Doc Intelligence extracts text + layout metadata for enterprise compliance workflows.

Cost note: predictable page-based pricing.

B · Vision + Context

GPT-4o vision plus contextual retrieval captures chart semantics and caption references.

Cost note: image-heavy corpora increase ingest costs fastest.

C · Managed Parse

LlamaParse handles complex PDFs and forms with automatic managed chunking.

Cost note: free tier for first 1,000 pages each month.

Quick Stack Chooser

☁️ Stack A

Large regulated teams that need end-to-end governance inside Azure.

Accuracy	95%
Setup	1-2 days
Lock-in	High lock-in
Framework	Azure AI Suite

🧩 Stack B

Accuracy-first teams willing to compose best-in-class providers.

Accuracy	97%
Setup	4-7 days
Lock-in	Low lock-in
Framework	LangGraph / Custom

🦙 Stack C

Small teams that need managed ingestion and quick production rollout.

Accuracy	94%
Setup	Hours
Lock-in	Medium lock-in
Framework	LlamaIndex