RAG from scratch: give your AI model memory
LLMs12 min read

RAG from scratch: give your AI model memory

By Editorial ·

Sample tutorial in MDX (allows components beyond Markdown).

RAG (Retrieval-Augmented Generation) connects a language model to a knowledge base, reducing hallucinations and keeping answers up to date.

The flow in 4 steps

  1. Index documents as embeddings in a vector database.
  2. Retrieve the most relevant chunks for the question.
  3. Augment the prompt with those chunks.
  4. Generate the answer based on the retrieved context.
# pseudocode for the retrieval step
query_emb = embed(question)
chunks = vector_db.search(query_emb, k=4)
prompt = f"Context:\n{chunks}\n\nQuestion: {question}"
answer = llm(prompt)

In the next tutorial, we implement each step with real code.