Tech & AI•10 min read

Building Production-Ready RAG Applications with Next.js 15 & Vector Databases

Rohan Verma

July 10, 2026

Building Production-Ready RAG Applications with Next.js 15 & Vector Databases

Summary & Key Highlights

Learn how to architect scalable Retrieval-Augmented Generation (RAG) applications using Pinecone, Pgvector, LangChain, and Next.js Server Components.

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Retrieval-Augmented Generation (RAG) has become the gold standard for connecting Large Language Models (LLMs) with custom, private enterprise data. Instead of fine-tuning expensive models, RAG retrieves relevant document chunks at runtime and feeds them as context to the model.

1. Document Chunking & Embeddings

The secret to accurate RAG responses isn't the LLM—it's the chunking strategy. Fixed-size chunking often splits sentences in half, causing loss of semantic context. Modern setups utilize recursive character text splitting combined with semantic metadata tags (such as author, created date, and document hierarchy).