LLM Evals Are Not Just Model Tests
📰 Medium · LLM
Evaluating LLMs requires considering the entire system, including prompts, outputs, latency, cost, and hallucinations, to ensure effective model deployment and maintenance
Action Steps
- Build an extraction pipeline to test LLMs in a real-world setting
- Run experiments to evaluate prompt effectiveness and output quality
- Configure metrics to measure latency and cost
- Test for hallucinations and other potential biases
- Apply evaluation results to refine model performance and system design
Who Needs to Know This
Data scientists and AI engineers benefit from understanding the complexities of LLM evaluation to improve model performance and reliability, while product managers and software engineers can apply these insights to develop more effective LLM-based systems
Key Insight
💡 LLM evaluation must consider the interplay between model performance, system design, and external factors to ensure reliable and effective deployment
Share This
💡 LLM evals are not just model tests, but a holistic assessment of the entire system #LLMs #AI
Key Takeaways
Evaluating LLMs requires considering the entire system, including prompts, outputs, latency, cost, and hallucinations, to ensure effective model deployment and maintenance
DeepCamp AI