Your RAG Pipeline Is Probably Useless. Here’s a Better Alternative
What happened
The conversation around Retrieval-Augmented Generation (RAG) is shifting from over-engineering pipelines to 'context engineering,' which systematically assembles all necessary context for LLMs to solve tasks. This approach addresses common RAG failures like retrieval irrelevance and context poisoning that make traditional pipelines "useless" in production.
Why it matters
AI Engineers facing underperforming RAG pipelines should stop over-engineering existing designs and instead evaluate their corpus size and query types to select the appropriate architecture, prioritizing long-context prompting for smaller corpora and structured retrieval for larger ones.
Topics
- Retrieval-Augmented Generation
- Long-Context LLMs
- Context Engineering
- RAG Failure Modes
Articles in this trend
- Your RAG Pipeline Is Probably Useless. Here’s a Better Alternative — KDnuggets
- Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer — Towards Data Science
- RAG in Production: The Retrieval Failures Nobody Writes About — LLM on Medium
- Your LLM Isn’t Dumb — It Just Lacks Your Context — Artificial Intelligence in Plain English - Medium
- GraphRAG vs Vector RAG: Which Retrieval Method is Best? — Analytics Vidhya