rafaelvp-db/databricks-llm-prompt-engineering

Examples of Prompt Engineering, Zero Shot Learning, Few Shot Learning and Retrieval Augmented Generation (RAG) using Hugging Face, Databricks and MLflow

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Experimental

This project helps machine learning engineers and data scientists refine how large language models (LLMs) respond to prompts for tasks like customer service. It provides tools and examples for experimenting with different prompting strategies and deploying optimized LLMs. You input various prompts and model configurations, and it helps you understand and improve the quality of the LLM's text output.

No commits in the last 6 months.

Use this if you are developing or fine-tuning large language models on Databricks and need to experiment with different prompt engineering techniques to improve model performance and deployment.

Not ideal if you are looking for a plug-and-play application for end-users or do not have experience with machine learning model development and deployment.

LLM-development prompt-engineering model-deployment natural-language-processing machine-learning-operations
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 5 / 25

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Stars

16

Forks

1

Language

Python

License

MIT

Last pushed

Sep 21, 2023

Commits (30d)

0

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