Compare inference, training, fine-tuning, prompting, and RAG.

Separates runtime model use from model training, fine-tuning, prompt design, and retrieval-augmented generation as different engineering levers.

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2CompareCompare with Core answer

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TL;DR

Inference is running a trained model to produce an output. Training creates model parameters from data, while fine-tuning further adapts an existing model with examples. Prompting changes the instructions and runtime context without changing model weights. RAG retrieves external information at request time and places selected evidence in the context. Prompting is usually the first lever for task framing, RAG helps with current or private knowledge, and fine-tuning can improve repeatable behavior or style when enough evidence justifies it.

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Practice focus

The ability to choose the least complex adaptation method that addresses behavior, knowledge, freshness, or control.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
These terms describe different points in the system lifecycle and solve different problems; treating them as interchangeable leads to expensive designs.
Inference is running a trained model to produce an output. Training creates model parameters from data, while fine-tuning further adapts an existing model with examples. Prompting changes the instructions and runtime context without changing model weights. RAG retrieves external information at request time and places selected evidence in the context. Prompting is usually the first lever for task framing, RAG helps with current or private knowledge, and fine-tuning can improve repeatable behavior or style when enough evidence justifies it.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
I choose from the failure. If the model lacks current product policy, fine-tuning is usually the wrong first answer because the fact will change; retrieval gives freshness and provenance. If the model repeatedly produces the wrong response shape despite clear instructions and constrained output, fine-tuning may be worth evaluating. Training a foundation model is a different scale of investment. These approaches can combine, but each adds data, evaluation, deployment, and maintenance work.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Using fine-tuning to keep frequently changing facts current.
Calling any prompt example fine-tuning.
Assuming RAG changes model weights.
Combining every technique before establishing a baseline.

Compare inference, training, fine-tuning, prompting, and RAG.

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

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