67%
Failure Rate Cut
Contextual + A mixed retrieval approach that combines multiple search signals instead of only one. + A second-pass model that reorders retrieved chunks so the most relevant ones are first.RAG SYSTEM ARCHITECTURE · 2025
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RAG Planner
Decision support for designing retrieval architectures and estimating ingestion plus operational costs before committing to implementation.Click any stack above to open a full-screen implementation popup with procedures, connection methods, and provider mix comparisons.
93-97%
Peak Accuracy
Stack B on structured corpora3.5x
Recall Gain
A mixed retrieval approach that combines multiple search signals instead of only one. vs Retrieval that uses only vector similarity, without keyword/BM25 scoring. searchHours
Time to Production
Stack C (LlamaCloud managed)The 3-Technique Foundation
Regardless of stack, these three techniques deliver a 67% reduction in retrieval failures.
- 1Combines lexical search and vector search so either signal can surface relevant content.
- 2Retrieval that enriches chunks and queries with context before search and ranking.
- 3A second-pass model that reorders retrieved chunks so the most relevant ones are first.
Image & Diagram Handling
A · OCR First
Azure document AI service for OCR, layout extraction, and structured field parsing. extracts text + layout metadata for enterprise compliance workflows.
Cost note: predictable page-based pricing.B · Vision + Context
GPT-4o vision plus Retrieval that enriches chunks and queries with context before search and ranking. captures chart semantics and caption references.
Cost note: image-heavy corpora increase ingest costs fastest.C · Managed Parse
LlamaIndex parsing service for complex PDFs, tables, and layout-aware extraction. handles complex PDFs and forms with automatic managed The process of splitting documents into smaller sections before indexing..
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 Dependency on a single vendor that makes migration harder and slower. |
| Framework | Azure AI Suite |
🧩 Stack B
Accuracy-first teams willing to compose best-in-class providers.
| Accuracy | 99.9% |
|---|---|
| Setup | 4-7 days |
| Lock-in | Low Dependency on a single vendor that makes migration harder and slower. |
| Framework | LangGraph / Custom |
🦙 Stack C
Small teams that need managed The pipeline stage that parses, chunks, embeds, and indexes documents. and quick production rollout.
| Accuracy | 94% |
|---|---|
| Setup | Hours |
| Lock-in | Medium Dependency on a single vendor that makes migration harder and slower. |
| Framework | LlamaIndex |