Study Note58 Experimental Query Generation: Using Sample Queries to Build Complex SQL Most of the queries we need help with are probably non-trivial. If you really just need to see all the rows in a table, you can likely figure that out without AI. Where AI really helps is when we want to ask and answer questions, which are almost never answered by showing you everything. However, these types of question-answering queries are often hard to craft. When you've finally identified.. 2025. 6. 22. The CLUE Technique: Grounding Generative AI The CLUE technique offers a systematic approach to preserving and reusing database context, enabling more efficient and accurate interactions with AI. With the CLUE technique, you can build a reusable starting point for your database conversations.C: Continuing Established ConversationsThe first element of the CLUE technique leverages the AI's ability to maintain context throughout a conversatio.. 2025. 6. 20. Grounding Generative AI: Avoiding Hallucinations When Working with Databases Why Hallucinations OccurOne of the most remarkable aspects of generative AI is its creative capacity—its ability to generate novel ideas, stories, and solutions. However, this same creative power can lead to problems when working with databases and SQL, where precision is paramount.When generative AI lacks sufficient context about your database environment, it attempts to "fill in the blanks" wi.. 2025. 6. 20. Chat with Your Data: Generative AI-Powered SQL Data Analysis The world of data analysis has transformed dramatically in recent years. With generative AI integrated into SQL data analysis, anyone can now talk to their data. As I've been learning data analysis, I've realized that AI-powered SQL tools are more than just technical aids they open up new possibilities for growth and learning. Yet, I sometimes worry: "Does relying on AI make me seem less skilled.. 2025. 6. 20. Tableau Practice 1 This week, I attended Tableau classes. Instead of listing out the detailed tool content and information one by one, I decided to upload the final practice work that I completed. Through this course, I created a dashboard. There are still many areas that need improvement, and I would like to upload various features to my blog. However, unlike Python code, there are limitations to how Tableau c.. 2024. 7. 4. Machine Learning [Regression] practice Machine Learning [Regression] practice Question 1.from sklearn.datasets import load_irisimport pandas as pdiris = load_iris()df = pd.DataFrame(iris.data, columns=iris.feature_names)df['target'] = iris.targetdf['target_name'] = iris.target_names[iris.target]#X standardscalefrom sklearn.preprocessing import StandardScalerscaler = StandardScaler()X, y = iris.data, iris.target#X 값 표준화X_sc = scaler.f.. 2024. 6. 13. 이전 1 2 3 4 ··· 10 다음
