This week Emmanuel Acheampong shares how to build LLM benchmarks that accurately measure performance on your tasks.

In this edition of our knowledge sharing publication, you’ll learn:

• 𝗪𝗵𝘆 𝗽𝘂𝗯𝗹𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗹𝗶𝗸𝗲 𝗠𝗠𝗟𝗨 𝗰𝗮𝗻 𝗺𝗶𝘀𝗹𝗲𝗮𝗱, when rising scores reflect gaming for leaderboards more than real capability in your domain.

• 𝗛𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗲𝘃𝗮𝗹, using high-stakes exams, verified answer keys, structured datasets, and clean prompt design.

• 𝗪𝗵𝘆 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗯𝗼𝘁𝗵 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗮𝗻𝗱 𝘀𝗶𝘁𝘂𝗮𝘁𝗲𝗱 𝘁𝗲𝘀𝘁𝘀, so you can separate general model competence from domain or regional knowledge gaps.

• 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗪𝗔𝗦𝗦𝗖𝗘 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝘆 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝘄𝗵𝗮𝘁 𝗽𝘂𝗯𝗹𝗶𝗰 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗺𝗶𝘀𝘀, especially when evaluating models for underrepresented communities and real educational stakes.

• 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆, from leakage and prompt consistency to subject-level weaknesses and failure modes that actually matter in deployment.

Read the full article for a practical framework to design LLM benchmarks around your users, your context, and the tasks your product really depends on