Explain RAG vs Fine-tuning.
RAG vs fine-tuning compares two ways of adapting AI models: Retrieval-Augmented Generation (RAG) connects models to external data in real time, while fine-tuning retrains the model to internalize new knowledge.
When to Use
RAG is best when information changes quickly (e.g., news, product catalogs) or is too large to store inside a model. Fine-tuning works well when the domain is stable, and responses must follow a specific style or format.
Example
A customer support bot may use RAG to fetch answers from updated documentation, while a fine-tuned model ensures responses always match the company’s tone.
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Why Is It Important
Choosing the right approach determines whether your AI is current and flexible (RAG) or specialized and efficient (fine-tuning).
Interview Tips
In interviews, define both clearly, give a practical use case, compare pros/cons, and highlight that combining them often produces the strongest results.
Trade-offs
RAG keeps responses fresh and reduces hallucinations but adds latency and retrieval complexity. Fine-tuning makes inference fast and domain-specific but requires costly retraining and risks outdated knowledge.
Pitfalls
Avoid assuming fine-tuned models will “know everything” or that RAG alone can fix model weaknesses. A hybrid often works best.
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