LLMs didn't kill feature engineering. Engineers did.
📰 Dev.to · Jignesh Maheshwari
Learn why LLMs didn't replace feature engineering and how engineers can still apply their skills to improve model performance
Action Steps
- Recognize that LLMs still require high-quality input data to perform well
- Identify areas where feature engineering can complement LLMs
- Apply feature engineering techniques to improve model performance
- Collaborate with data scientists to integrate feature engineering into LLM workflows
- Monitor and evaluate the impact of feature engineering on LLM performance
Who Needs to Know This
Data scientists and engineers can benefit from understanding the role of feature engineering in improving LLM performance, and how to apply their skills to drive better model outcomes
Key Insight
💡 Feature engineering is still a crucial step in improving LLM performance, and engineers have a key role to play in driving model outcomes
Share This
🚀 LLMs didn't kill feature engineering! Engineers can still drive model performance with clever feature engineering 🤖
Key Takeaways
Learn why LLMs didn't replace feature engineering and how engineers can still apply their skills to improve model performance
Full Article
Somewhere around when LLMs started eating every roadmap, a quiet belief took over a lot of teams. If...
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