Our firm is experimenting with using generative AI to write Python code for data visualization. However, we've caught it making up column names and even fabricating small trends in the summary. For those of you integrating AI into your analytics workflow, what safeguards are you putting in place to ensure data integrity and accuracy?
3 answers
We’ve implemented a "Human-in-the-loop" verification step for every AI-generated report. We also use a technique called "Retrieval-Augmented Generation" (RAG) where we feed the LLM the actual schema of our database as a reference before it writes any code. This prevents the "imaginary column" issue. Additionally, you should have a secondary script that runs basic summary statistics (min, max, mean) to compare against the AI's output. Never let an LLM directly interpret data for a final presentation without a senior analyst reviewing the underlying logic and the source code it produced.
Are you providing the AI with a "Data Dictionary" in your prompt, or are you just asking it to "analyze this CSV" and hoping for the best?
Automated unit testing for the generated code is the only way to be 100% sure. If the code doesn't pass the test, don't trust the output.
Exactly, Lisa. Testing the code is just as important as testing the data itself when you're using automation tools like this.
We were just uploading the CSV, Paul. After your comment, we started including a detailed markdown file describing every field and its constraints. The hallucinations dropped by almost 80%. It turns out the AI just needed more context to understand what was 'impossible' in our specific dataset. We also added a prompt instruction to 'explain your reasoning step-by-step,' which helps us spot logic errors faster.