PyData Eindhoven 2023
Eindhoven, The Netherlands —
LLM data extraction: prompt engineering, JSON output modes, Function calling, Pydantic schemas, evaluation and a GPT 3.5 vs GPT 4 vs PaLM showdown.
Onboard the LLM hype-train! 🚂 Are you curious about what LLM's (Large Language Models) can do for you? One of their use cases is to enrich datasets, by converting text into structured data. This can have huge benefits. Useful facts that were previously hidden inside a large piece of text can now be unveiled, allowing your customers to more accurately filter and query your data.
Talk name: Dataset enrichment using LLM's ✨
Links
GitHub - dunnkers/dataset-enrichment-with-llms: Excerpt of the original code put up for display
Excerpt of the original code put up for display. Contribute to dunnkers/dataset-enrichment-with-llms development by creating an account on GitHub.
dunnkers
GitHub repository with example code used to support the presentation.
