How To Scalably Test LLMs [TestĪ¼ 2024]
Blog post from TestMu AI
In this session with Anand Kannappan, Co-founder and CEO of Patronus AI, the focus is on scalable testing of large language models (LLMs). The main challenges discussed include handling computational cost and complexity while maintaining efficiency. Key points covered in the session include creating diverse test cases, avoiding reliance on weak intrinsic metrics, and exploring new evaluation methods beyond traditional benchmarks. Anand also emphasizes the importance of combining both intrinsic and extrinsic evaluations for a comprehensive assessment of LLM performance.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 44 | 3,889 | 441 | 129 | +7% |
| RAG | 30 | 1,936 | 254 | 78 | -19% |
| Reinforcement learning | 6 | No monthly metrics for this publish month. | |||
| AI Model Fine-tuning | 3 | 628 | 146 | 67 | -32% |
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