AI & Engineering8 min read • Published 2026-08-10
How to Implement RAG Architecture in Modern Web Applications
A practical guide to integrating Retrieval-Augmented Generation (RAG) using OpenAI embeddings, vector databases like Qdrant, and Next.js for reliable AI search.
Large Language Models (LLMs) are powerful, but they lack real-time access to private business documents and often generate plausible hallucinations when asked about specific enterprise data.
### What is Retrieval-Augmented Generation (RAG)?
RAG bridges this gap by retrieving relevant factual context from a custom database before sending prompts to the LLM.
### Key Components of a Web RAG Pipeline:
1. **Document Parsing & Chunking:** Breaking long PDFs or text into manageable, semantic chunks (e.g. 500 characters with 50-character overlap).
2. **Vector Embeddings Generation:** Passing chunks through OpenAI's `text-embedding-3-small` to convert text into mathematical vector representations.
3. **Vector Database Indexing:** Storing vectors in high-performance engines like Qdrant or Pinecone.
4. **Semantic Retrieval:** Performing cosine similarity search when a user asks a question to extract top matching chunks.
5. **Contextual Prompt Injection:** Constructing an augmented prompt: *"Answer the question strictly using the provided context."*