RAG Clearly Explained!

📰 Medium · RAG

Learn how RAG combines retrieval, embeddings, vector databases, and LLMs for efficient information retrieval

intermediate Published 26 Jun 2026

Action Steps

Build a simple RAG model using a vector database to store embeddings
Run a query to retrieve relevant information from the database
Configure the LLM to generate text based on the retrieved information
Test the RAG model using a sample dataset
Apply the RAG model to a real-world application, such as question answering or text summarization

Who Needs to Know This

Data scientists, ML engineers, and software developers can benefit from understanding RAG to improve their information retrieval systems

Key Insight

💡 RAG uses a combination of retrieval, embeddings, vector databases, and LLMs to efficiently retrieve and generate text

Key Takeaways

Learn how RAG combines retrieval, embeddings, vector databases, and LLMs for efficient information retrieval

simple mental model of how retrieval, embeddings, vector databases, and LLM work together. Continue reading on Medium »