Santosh Mahale writes that most teams default to vector-database RAG without evaluating whether it fits their data and query patterns, when the retrieval architecture is the primary lever for production success. He compares three options—Traditional (semantic search via embeddings), Vectorless (exact lookups via SQL, BM25, APIs, or graph traversal with no vector store), and Hybrid (both retrieval paths merged and reranked)—and recommends starting with the simplest approach that solves the use case, measuring where it fails, then adding complexity only where data demands it.
- Most RAG failures are retrieval failures (wrong context reaching the LLM), not model failures
- Vectorless RAG is underused; structured-data workloads like log analysis, K8s event lookups, and compliance records often outperform Traditional RAG with far less infrastructure
- Hybrid RAG is the eventual landing spot for most enterprise deployments but adds two retrieval paths, a merge step, and a reranker to maintain
- The article positions RAG variants within a broader stack: LLM → RAG architectures → agents → MCP → agentic systems, each solving a different layer
- Most RAG failures are retrieval failures (wrong context reaching the LLM), not model failures
- Vectorless RAG is underused; structured-data workloads like log analysis, K8s event lookups, and compliance records often outperform Traditional RAG with far less infrastructure
- Hybrid RAG is the eventual landing spot for most enterprise deployments but adds two retrieval paths, a merge step, and a reranker to maintain
- The article positions RAG variants within a broader stack: LLM → RAG architectures → agents → MCP → agentic systems, each solving a different layer
>How AI architecture prevents plausible but wrong analytics
The article describes a hybrid inference architecture that prevents inaccurate data analysis by separating probabilistic LLM reasoning from deterministic code execution.
- LLMs frequently generate plausible but incorrect analytical results.
- Hybrid model separates interpretation tasks from mathematical execution.
- Analysis Planner converts natural language into structured JSON instructions.
- Analysis Engine runs predefined Python scripts to ensure accuracy.
- Semantic mapping files decouple user requests from complex datasets.
Guide
The article describes a hybrid inference architecture that prevents inaccurate data analysis by separating probabilistic LLM reasoning from deterministic code execution.
- LLMs frequently generate plausible but incorrect analytical results.
- Hybrid model separates interpretation tasks from mathematical execution.
- Analysis Planner converts natural language into structured JSON instructions.
- Analysis Engine runs predefined Python scripts to ensure accuracy.
- Semantic mapping files decouple user requests from complex datasets.
Guide
-set hive.exec.orc.split.strategy=ETL; -- this will work only for specific values scan, if full table scan will be required anyway, use default (HYBRID) or BI.
