AI Aṣẹ̀dá

Iṣelọpọ ti ipilẹṣẹ le wulo laisi jijẹ otitọ, atilẹba ni ori ofin, tabi deede fun atẹjade. Awọn agbara wọnyi nilo awọn sọwedowo lọtọ.

Awọn gbigba bọtini

• Ṣe ibaramu igbelewọn si artifact ti a ṣẹda.
• Ṣe iyatọ awọn otitọ orisun lati awọn afikun awoṣe.
• Ṣe atunyẹwo ati ṣe atunṣe.

Different generation systems use different mechanisms. An autoregressive text model predicts successive tokens. Diffusion-based image systems learn to transform noisy representations into samples. These are model families, not guarantee about every product or implementation. A prompt specify a task and context, but a complete application can also retrieve documents, invoke tools, or filter outputs. Supplying source material can improve relevance while still leaving room for omissions and unsupported claims. Separate what a source states from what the model infers. Evaluate outputs according to their use. For summarization, check factual consistency and coverage. For code, inspect behavior and run significant tests. For images or audio, review artifacts, consent, and the intended use of recognizable people or protected material. One broad preference score cannot settle all of these questions. Use a workflow with a clear review point and a way to fix mistakes. Record the model version, prompt, relevant source material, and settings when reproducibility matters. Iran keji le yatọ, nitorinaa tọju iṣelọpọ gangan ti a lo ninu ipinnu tabi ohun elo ti a tẹjade.

1. Kọ akọsilẹ ipade kan pẹlu awọn ipinnu mẹta, awọn ibeere ṣiṣi meji, ati imọran idaniloju kan.
2. Beere fun akopọ kan, lẹhinna ṣe aami alaye kọọkan ti ipilẹṣẹ bi atilẹyin, ti a fi silẹ, tabi ti a fi kun kọja akọsilẹ naa.
3. Ṣe atunyẹwo eyikeyi imọran ti a gbekalẹ bi ipinnu ikẹhin ki o mu pada eyikeyi oniwun ti o padanu tabi akoko ipari.

Ọna atunyẹwo apejuwe yii ṣayẹwo iṣootọ si orisun kan dipo idajọ nikan ni irọrun ti prose.