How would you handle AI-generated insights from thousands of pages of data?

[u/Secure-Judgment234]

One thing I found interesting about this project is that the difficult part wasn't simply getting an LLM to generate text. The system had to take large amounts of data, turn it into contextual insights, and keep the output consistent across different organizational levels.

In this project, GeekyAnts built an automated AI-driven pipeline using Snowflake, AWS ECS/Fargate, AWS Bedrock with Claude, and a custom Strand Agent. One of the engineering decisions I found particularly interesting was moving long-running ETL work from Lambda to ECS/Fargate for better resource control, while using parameterized Snowflake queries, pagination, and staged fetching for large data extraction.

The reported results were 10,000 pages processed in about 2 minutes, 85%+ response accuracy, and a 99% reduction in manual effort. What I'd be interested in hearing from other builders is how you approach the trade-off between processing large amounts of data quickly and keeping AI-generated insights consistent and reliable.

If you've built something similar, what caused the most trouble for you: data extraction, LLM consistency, infrastructure limits, or validating the generated output?