AWS Machine Learning

SEPTEMBER 22, 2023

Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks. yaml ppo_hh.py

Engineering 97

Engineering Feedback industry standards Benchmark 97

Callminer

AUGUST 1, 2017

Bill Dettering is the CEO and Founder of Zingtree , a SaaS solution for building interactive decision trees and agent scripts for contact centers (and many other industries). Interactive agent scripts from Zingtree solve this problem. Agents can also send feedback directly to script authors to further improve processes.

Contact Center 334

Contact Center Call Center Average Handle Time Real estate 334

AWS Machine Learning

MARCH 13, 2024

. * The `if __name__ == "__main__"` block checks if the script is being run directly or imported. To run the script, you can use the following command: ``` python hello.py ``` * The output will be printed in the console: ``` Hello, world! Evaluate model on test set, compare to benchmarks, analyze errors and biases.

Benchmark 106

Benchmark Scripts APIs Feedback 106

AWS Machine Learning

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. Building large language models (LLMs) from scratch or customizing pre-trained models requires substantial compute resources, expert data scientists, and months of engineering work.

Best practices 105

Best practices Engineering Chatbots Enterprise 105

AWS Machine Learning

APRIL 17, 2023

There is no industry standard for distillation, and many techniques are experimental. Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models.

Engineering 78

Engineering Benchmark Scripts industry standards 78

AWS Machine Learning

NOVEMBER 29, 2023

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. These benchmarks have leaderboards that can be used to compare and contrast evaluated models.

Benchmark 83

Benchmark Metrics Engineering APIs 83