I built a RAG pipeline from scratch, and one wrong answer made me dive even deeper into AI Engineering

May 30, 2026 · 2:53 AM UTC ·5 min read · 0 reactions · 0 comments · 33 views

TL;DR · WeSearch summary

The author shares their journey of building a Retrieval-Augmented Generation (RAG) pipeline from scratch, transitioning from backend engineering to AI Engineering. They emphasize the importance of understanding the fundamentals and the role of embeddings in the process. A key learning moment occurred when the system provided an incorrect response, prompting deeper exploration into AI concepts.

Key facts

▪The author has a background in backend engineering and decided to pivot towards AI Engineering.
▪RAG stands for Retrieval-Augmented Generation, which enhances LLMs by fetching relevant information at query time.
▪Embeddings are vectors that represent the semantic meaning of text, allowing for more nuanced searches in a vector database.

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Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/felipearaujobs/i-built-a-rag-pipeline-from-scratch-and-one-wrong-answer-made-me-dive-even-deeper-into-ai-4npg
Publication time	Sat, 30 May 2026 02:53:17 +0000
Retrieval time	2026-05-30T03:11:55.368Z
Last seen	2026-05-30T03:11:55.368Z
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	6mAri3ZiWOV2 · 2 stories
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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Unknown

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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 === 3959297) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Felipe Araújo Posted on May 30 I built a RAG pipeline from scratch, and one wrong answer made me dive even deeper into AI Engineering #ai #rag #softwareengineering #python A backend engineer's first step into AI Engineering: embeddings, vector search, and the chunking bug that made everything click. Why I decided to pivot toward AI Engineering I have been a backend engineer for a while now: TypeScript, NestJS, distributed systems, APIs in production. I like that work.

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