My RAG pipeline couldn't find the CEO — here's how I fixed it with hybrid retrieval

Jun 3, 2026 · 3:04 PM UTC ·4 min read · 0 reactions · 0 comments · 47 views

TL;DR · WeSearch summary

The article discusses improvements made to a RAG pipeline that initially struggled to retrieve specific information such as the CEO's name. The author identified that the issue stemmed from a dense table of information that muddled the semantic search results. By implementing a hybrid retrieval approach combining semantic and keyword searches, the pipeline successfully retrieved accurate answers.

Key facts

▪The initial RAG pipeline scored 17 out of 19 on the test set but failed to answer specific questions about the CEO and employee count.
▪The problem was due to a dense 'Company snapshot' table that averaged multiple topics, weakening the search results.
▪The author implemented a hybrid retrieval method using FAISS for semantic similarity and BM25 for keyword matching, merging results with Reciprocal Rank Fusion.

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Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/santanu_mohanta_29/my-rag-pipeline-couldnt-find-the-ceo-heres-how-i-fixed-it-with-hybrid-retrieval-43ao
Publication time	Wed, 03 Jun 2026 15:04:11 +0000
Retrieval time	2026-06-03T15:12:09.866Z
Last seen	2026-06-03T15:12:09.866Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	uRpjKaksUOeR
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

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WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

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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 === 3959064) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Santanu Mohanta Posted on Jun 3 My RAG pipeline couldn't find the CEO — here's how I fixed it with hybrid retrieval #rag #python #ai #fastapi In my last post, I built a RAG pipeline from scratch — no LangChain, just FastAPI + FAISS. It scored 17/19 on my test set. But two questions failed: "Who is the CEO?" — couldn't find it "How many employees does Zentara have?" — couldn't find it Both answers were right there on page 1.

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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).

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