Moderate Evals Question 116 of 223

How do faithfulness and answer relevance differ in RAG evals?

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

PICTURE THIS: AN LLM TURN

Text inTokens
TransformerAttention
Text outNext token

Simple meaning

Faithfulness asks whether claims are supported by retrieved context.

1

WHY — Tokens instead of words?

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

This is a process

question about Evals.

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
    Faithfulness asks whether claims

    are supported by retrieved context.

  2. 2
    Relevance asks whether the

    answer addresses the user question.

  3. 3
    An answer can be

    faithful and off-topic, or relevant and fabricated.

  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
“Relevance asks whether the answer addresses the user question.”
Break into beats
Relevanceaskswhethertheansweraddresses
Speaking order
2987408337471632900

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

Key takeaway

Faithfulness asks whether claims are supported by retrieved context. Relevance asks whether the answer addresses the user question.

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