Data Analysis
What is data analysis?
Data analysis is the process of examining, manipulating, and visualizing data to extract insights and make informed decisions. It involves slicing data in different ways, choosing appropriate visualization methods, identifying patterns and outliers, and iteratively exploring results to understand what the data reveals.
In AI product development, data analysis is essential for tasks like evaluating model performance through evals, understanding error patterns in LLM responses, and turning raw data exports into actionable insights.
What tools are used for data analysis in AI development?
Python and Jupyter Notebooks are commonly used for data analysis because they make it easy to:
- Write and execute code in the context of your notes
- Manipulate data (using libraries like Pandas)
- Create visualizations
- Inspect traces and do error analysis
- Iteratively explore results
Jupyter Notebooks are particularly valuable because you can write pseudocode, then write real code, execute it and see the results, then capture your thoughts about those results—all in one file. This makes the exploratory nature of data analysis much more manageable.
LLMs themselves can also help with data analysis. You can use tools like ChatGPT to turn raw analytics exports into visualizations, identify patterns, and determine the best ways to slice and examine your data.
Why is data analysis important for AI products?
When building AI products, you need to understand how well your system is performing. Data analysis allows you to:
- Dig into eval results to diagnose why the LLM is getting certain responses wrong
- Identify which traces are failing and understand the patterns
- Score your LLM-as-Judge evals to see how well they're working
- Figure out what changes are needed to reduce errors
Rather than guessing at improvements, data analysis helps you make evidence-based decisions about what to change in your prompts, context, or system architecture.
Learn more:
- How I Designed & Implemented Evals for Product Talk's Interview Coach
- 21 Ways to Use AI at Work (And Build Your AI Product Toolbox)
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