Ship AI Features Without the Fire Drill: Write the Eval First
📰 Dev.to AI
Ship AI features efficiently by writing evaluations first to avoid common pitfalls in LLM scoring pipelines
Action Steps
- Define the evaluation metrics for your LLM model before training
- Determine the key performance indicators (KPIs) that align with human judgment
- Design a testing framework to validate model outputs against real-world data
- Iterate on model refinement based on evaluation results
- Implement a feedback loop to continuously improve model performance
Who Needs to Know This
Data scientists and AI engineers can benefit from this approach to ensure their LLM models produce relevant and useful results, saving time and resources in the long run
Key Insight
💡 Writing evaluations first helps ensure LLM models produce relevant results, saving time and resources
Share This
💡 Write evaluations first to ship AI features efficiently and avoid the 80/20 trap #AI #LLM #Evaluation
Key Takeaways
Ship AI features efficiently by writing evaluations first to avoid common pitfalls in LLM scoring pipelines
Full Article
I've watched teams spend weeks refining an LLM scoring pipeline, only to run it against real data and discover that many of the scores are useless. The model rewards keyword density over actual relevance. The output looks structured. The numbers are in range. But the results don't match what a human would judge. That's the moment you realize: you don't ship AI features by writing prompts first. You ship them by writing the evaluation first. The 80/20 Trap Nobody Talks About </
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