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