RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source)
The article discusses the author's testing of seven retrieval-augmented generation (RAG) configurations to build an open-source system called SEQUOIA. The results revealed that many popular RAG systems underperform in real-world scenarios, with SEQUOIA emerging as the most effective configuration. Key insights include the importance of step-back prompting and the viability of local LLMs for benchmarking.
- ▪The author benchmarked seven RAG configurations against real-world tasks after 20+ hours of compute time.
- ▪SEQUOIA Pro achieved the highest precision and recall among the tested methods, while LightRAG underperformed despite its hype.
- ▪Step-back prompting improved retrieval quality significantly, demonstrating that academic benchmarks do not always reflect production realities.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3957324) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ai developer Posted on May 28 • Originally published at t.me RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) #rag #llm #ai #career RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source) After 20+ hours of compute time on local hardware, I benchmarked 7 RAG configurations against real-world tasks. The results surprised me — and changed how I think about retrieval architecture. Why This Matters RAG is everywhere in 2026.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
