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From Haikus to Helper - Wrangling Agentic LLM's
This talk covers building an LLM-powered product for data transformation and enrichment, detailing architecture, SQL and vector retrieval, schema prediction, and agentic actions like report generation.
We built a product that uses LLM’s for data transformations + enrichment ( think spreadsheet) but also supports Agentic responses/actions on these datasets.
I’ll do a brief demo of the product and describe the architecture and challenges productizing augmentation and agentic behaviours.
We’ll walk through adding an AI enrichment column, and specifically cover:
- The path of data through the system: Ingestion => SQL / Vectorization => Retrieval,
- LLM touch points - importance estimation, schema prediction, extraction/augmentation.
Use the agent to analyze results and support the user:
- Prepare a report, have it posted to slack
Datagrid is an Agentic AI leveraging multi-LLMs to enrich data and automate tasks.
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