The Evolution of Retrieval-Augmented Generation (RAG) Pipelines
This article discusses the changes in Retrieval-Augmented Generation (RAG) pipelines over time, highlighting how the technology has evolved from a simple 5-stage process in 2023 to a more sophisticated and effective approach in 2026.
Why it matters
This article highlights the importance of continuously evolving AI/ML systems to keep up with the rapid advancements in the field.
Key Points
- 1The 2023 RAG pipeline was a basic demo that cleared the low bar at the time, but is now outdated compared to modern solutions.
- 2The 2026 RAG pipeline incorporates query rewriting to improve semantic search, and uses a hybrid search approach combining dense vector and keyword-based retrieval.
- 3The article emphasizes the importance of constantly iterating and improving the individual components of the RAG pipeline to keep up with the rapidly advancing state of the art.
Details
The article describes how the traditional 5-stage RAG pipeline (query -> embed -> ANN search -> top_k chunks -> prompt -> LLM -> answer) has been significantly improved over time. In 2023, this pipeline was commonly used, but had several failure modes, such as queries not matching the embedding space, chunk boundaries splitting relevant information, and the model hallucinating citations. The 2026 stack keeps the 5-stage outline, but each stage has been rebuilt. The key changes include: 1. Query rewriting: Instead of directly embedding the user's raw query, the new stack uses a language model to rewrite the query into 3 optimized retrieval queries, improving semantic search performance. 2. Hybrid search: The 2026 stack uses a combination of dense vector search and keyword-based retrieval (BM25, BM25F, SPLADE), merging the results using Reciprocal Rank Fusion. This addresses the limitations of pure dense vector search, which struggles with exact token matching. These advancements demonstrate the rapid evolution of RAG pipelines, where the bar has been raised significantly since the initial 2023 version. Constantly iterating and improving the individual components is crucial to keeping up with the state of the art in AI-powered search and generation.
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