Exploratory Data Analysis Leads to Better AI Products
The article emphasizes the importance of Exploratory Data Analysis (EDA) in building effective AI products. It highlights how understanding the characteristics and relationships within data can lead to more efficient AI processing and cost savings.
Why it matters
Effective Exploratory Data Analysis is crucial for building AI products that deliver value while optimizing costs and resources.
Key Points
- 1EDA helps uncover data attributes, strengths, and anomalies before feeding it to AI models
- 2Rule-based classification can handle a significant portion of the workload, reducing unnecessary AI calls
- 3Analyzing the data can reveal opportunities to build better rules and focus human review on low-confidence areas
Details
The article discusses the author's experience implementing a Question-Answering system over a PDF document. Before feeding the data to the AI model, the author spent significant time on EDA to understand the data's nature and characteristics. This allowed them to identify areas where rule-based classification could handle the workload confidently, avoiding unnecessary AI processing and associated costs. The author found that over 40% of the document chunks could be classified using simple rules, and two categories made up over 50% of the total. This insight enabled them to focus on improving the rules for those high-volume areas or strategically allocate human review to the low-confidence chunks where AI could not handle the nuances. The key message is that understanding your data through EDA can lead to more efficient and cost-effective AI solutions.
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