Part 8/10:

He shared personal experience with complex datasets, demonstrating that these AI-powered methods work remarkably well even with 20-30 fields of data, delivering near-magical results.

Future Outlook and Strategic Recommendations

Mangjunad urged organizations to consider integrating these AI techniques into their data engineering workflows proactively. The key takeaways include:

• Building robust system prompts that enforce consistency.

• Grooming prompts over time for increased accuracy.

• Embedding scripts and AI logic into existing pipelines effortlessly.

• Moving towards smarter, self-healing data infrastructure that can handle schema and volume changes dynamically.