Moderate RAG Question 91 of 223

What is query rewriting in RAG?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: AN LLM TURN

Text inTokens

TransformerAttention

Text outNext token

Simple meaning

Query rewriting uses an LLM or rules to expand, correct, or split the user question into better search strings.

WHY — Tokens instead of words?

LLMs use tokens (not full words) because it helps them:

RAG questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to GenAI / LLM work.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

STEPS — What happens with tokens?

Before the model can read a sentence, it goes through these steps:

1
Query rewriting uses an
LLM or rules to expand, correct, or split the user question into better search strings.
2
Token IDs
Chatty follow-ups become standalone queries.
3
Better queries often beat
a bigger embedding model.
4
Context mix
Attention looks at nearby tokens together.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

EXAMPLE — See it in action

Let's see how a real sentence is tokenized (tokens may vary by model):

Input text

“Chatty follow-ups become standalone queries.”

Tokenized output

Chattyfollowupsbecomestandalonequeries

Token IDs (example)

2987408337471632900

Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).

Key takeaway

Query rewriting uses an LLM or rules to expand, correct, or split the user question into better search strings. Chatty follow-ups become standalone queries.