RAG: what actually matters

1 December 2026 | 6 min read | AI, LLM, RAG

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The question

How do you make a model answer questions about your documents? That is what RAG is for.

The big picture

RAG pipeline The four steps of a RAG system.

Under the hood

def chunk(text, size=500, overlap=50):
    chunks = []
    start = 0
    while start < len(text):
        chunks.append(text[start:start + size])
        start += size - overlap
    return chunks

Choice	Why it matters
Chunk size	Too small loses context, too large adds noise

Measure first. Most RAG problems are retrieval problems.

Key takeaways

Retrieval quality limits answer quality.

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