"Prompt engineering” refers to efforts to extract accurate, consistent, and fair outputs from large generative models, such text-to-image synthesizers or large language models (LLMs). LLMs are trained on large-scale bodies of text, so they encode a great deal of factual information about the world. But they’re trained to produce sequences of words that are probable in the general case — not accurate in the particular case.

What is a prompt?

• Text given to a pre-trained model for a prediction task

Components of Prompt

• Context
• Task (Instruction, question etc.)
• Image/Text
• Training samples
• Generative Model

1. Several types of prompt engineering include:
   1. Zero shot
   2. One shot and Few shot
   3. Chain of thought
2. Advanced concepts on prompt engineering
3. Adversarial Prompt engineering

Zero-shot prompting enables a model to make predictions about previously unseen data without the need for any additional training.

This is in contrast to traditional machine learning techniques, which require a large amount of labeled training data to make accurate predictions.

In the context of prompt engineering, zero-shot learning can be used to generate natural language text without the need for explicit programming or pre-defined templates.

One-shot prompting is used to generate natural language text with a limited amount of input data such as a single example or template.

One-shot prompting can be combined with other natural language processing techniques like dialogue management and context modeling to create more sophisticated and effective text generation systems.

Few-shot prompting is a technique where the model is given a small number of examples, typically between two and five, in order to quickly adapt to new examples of previously seen objects.

Few-shot learning can be used in the context of prompt engineering, to create natural language text with a limited amount of input data. Although it requires less data, this technique can allow for the creation of more versatile and adaptive text generation models.

By using advanced techniques such as few-shot prompting, it is possible to create natural language generation models that are more flexible, adaptable, and engaging for human users.

Chain-of-thought (CoT) prompting improves the reasoning ability of LLMs by prompting them to generate a series of intermediate steps that lead to the final answer of a multi-step problem.

In this lab, we are going to uncover how to setup SageMaker Studio for prompt engineering utility and use cases with different types of prompt engineering techniques.

• Navigate to SageMaker Studio.
• Click on Home on the left panel.
• Click on Deployments and navigate to Models,notebooks,solutions link within SageMaker Jumpstart in the drop-down menu. <<<<<<< HEAD

• Under Foundation Models search for FLAN-T5 XL model and click on view model.

• As executed in lab1, in a similar way, deploy the Flan-T5 XL model. You can change the deployment configuration or security settings based on the desired settings but you can go ahead and click on deploy for this lab. For deployment of the LLM models -- you can also refer to : https://github.com/aws/amazon-sagemaker-examples/tree/main/inference/generativeai/llm-workshop.

• Take note of the model Endpoint details: Endpoint arn, Endpoint name, and the model location. IT may take 5-10 minutes to deploy the model endpoint. Endpoint status should switch to In Service in some-time. You can also scroll down on the page to open a demo notebook in order to query your endpoint from Studio.

• Now that our endpoint is created (which can also be seen in SageMaker-Studio dashboard), navigate to studio-playground-ui folder that you cloned.

• Within the folder, you will see main.py file and a templates folder. Inside templates folder open FLANT5-JS.template.json file with editor option. Replace the endpoint_name with your endpoint name created in the previous steps and save the file.

• Check the instructions for creating GUI within the environment using Streamlit present in start_playground.txt file. In order to launch the GUI, click on File within SageMaker Studio tab, then click on New and navigate to Terminal.

  • Within the Terminal, run the following command: pip install boto3 streamlit streamlit-ace -Uq
  • Please ignore any warnings or errors related to pip's dependency...
  • Next, run the next command found in start_playground.txt -- streamlit run main.py. Make sure you are in the right directory to run this command in the terminal (amazon-sagemaker-generativeai/studio-playground-ui/). Once the command is executed, you can view your Streamlit app in your browser.
  • Copy the URL for SageMaker Studio and update the same by appending /proxy/<port_no>/. You would have received the port number after running the Streamlit app. Domain URL should look similar to https://d-lcxav5wg2gdg.studio.us-east-1.sagemaker.aws/jupyter/default/proxy/<port_no>/

• Now we can get started with the model playground and run prompt engineering on the deployed model endpoint within our environment in a secured fashion.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.