Moderate RAG Question 206 of 223

How do you choose chunk size 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

Balance context completeness versus retrieval precision.

1

WHY — Tokens instead of words?

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

This is a process

question about RAG.

Panels listen for order,

trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

2

STEPS — What happens step by step?

Before you speak the answer, walk the interviewer through these steps:

  1. 1
    Balance context completeness versus

    retrieval precision.

  2. 2
    Why it exists

    Overlap reduces boundary cuts.

  3. 3
    How it works

    I evaluate answer quality empirically.

  4. 4
    Give an example

    One tiny concrete case you can say aloud.

  5. 5
    Common mistake

    What juniors usually get wrong.

  6. 6
    Close

    When you pick this over the alternative.

3

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line
“Overlap reduces boundary cuts.”
Break into beats
Overlapreducesboundarycuts
Speaking order
29874083374716

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Balance context completeness versus retrieval precision. Overlap reduces boundary cuts.

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