https://github.com/build-on-aws/generative-ai-prompt-engineering
Sample code that helps us explore the world of generative AI through prompt engineering. It provides the resources for experimenting with this emerging skill through demos and sample applications such as simple chatbots.
https://github.com/build-on-aws/generative-ai-prompt-engineering
ai chatboot generative-ai gpt prompt-engineering
Last synced: 5 days ago
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Sample code that helps us explore the world of generative AI through prompt engineering. It provides the resources for experimenting with this emerging skill through demos and sample applications such as simple chatbots.
- Host: GitHub
- URL: https://github.com/build-on-aws/generative-ai-prompt-engineering
- Owner: build-on-aws
- License: mit-0
- Created: 2023-05-25T22:01:55.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-31T01:55:27.000Z (almost 2 years ago)
- Last Synced: 2025-03-25T03:12:17.428Z (22 days ago)
- Topics: ai, chatboot, generative-ai, gpt, prompt-engineering
- Language: Jupyter Notebook
- Homepage:
- Size: 41 KB
- Stars: 32
- Watchers: 9
- Forks: 16
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
## Generative AI - Prompt Engineering
### Welcome.
Sample code that helps us explore the world of generative AI through prompt engineering. It provides the resources for experimenting with this emerging skill through demos and sample applications such as simple chatbots.
### Notebooks:
- [Prompt Engineering - Chatbot](prompt-engineering-chatbot/prompt-engineering-chatbot.ipynb)
- This notebook has been designed, written and tested to run for free on [Amazon SageMaker Studio Lab](https://studiolab.sagemaker.aws/) with GPU. Studio Lab is a free machine learning (ML) development environment that provides compute and storage (up to 15GB) at no cost with NO credit card required.
- In this notebook we will look at a few prompt engineering techniques. We will experiment by loading a - relatively small - 3 billion parameter Large Language Model (LLM) within the notebook environment itself and using zero-shot, one-shot and few-shot in context learning prompts and see the response from the model. We will then use the techniques we have explored to build a simple chatbot!
## Security
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This library is licensed under the MIT-0 License. See the [LICENSE](LICENSE) file.