Overview
This lab explores prompt engineering and best practices for designing effective prompts to improve the quality of your LLM-generated responses. You'll learn how to craft prompts that are concise, specific, and well-defined, focusing on one task at a time. The lab also covers advanced techniques like turning generative tasks into classification tasks and using examples to enhance response quality. For further exploration, refer to the official documentation on prompt design.
Gemini
Gemini is a family of powerful generative AI models developed by Google DeepMind, capable of understanding and generating various forms of content, including text, code, images, audio, and video.
Gemini API in Vertex AI
The Gemini API in Vertex AI provides a unified interface for interacting with Gemini models. This allows developers to easily integrate these powerful AI capabilities into their applications. For the most up-to-date details and specific features of the latest versions, please refer to the official Gemini documentation.
Gemini Models
Gemini Pro: Designed for complex reasoning, including:
Analyzing and summarizing large amounts of information.
Sophisticated cross-modal reasoning (across text, code, images, etc.).
Effective problem-solving with complex codebases.
Gemini Flash: Optimized for speed and efficiency, offering:
Sub-second response times and high throughput.
High quality at a lower cost for a wide range of tasks.
Enhanced multimodal capabilities, including improved spatial understanding, new output modalities (text, audio, images), and native tool use (Google Search, code execution, and third-party functions).
Prerequisites
Before starting this lab, you should be familiar with:
Objectives
In this lab, you will learn how to:
Get started with prompt engineering using the Google Gen AI SDK
Apply best practices for prompt design, including conciseness, specificity, and task definition
Explore various text generation use cases with the Google Gen AI SDK, such as:
Ideation
Question answering
Text classification
Text extraction
Text summarization
Setup and requirements
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.
This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
- Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.
- Time to complete the lab—remember, once you start, you cannot pause a lab.
Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.
How to start your lab and sign in to the Google Cloud console
Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:
The Open Google Cloud console button
Time remaining
The temporary credentials that you must use for this lab
Other information, if needed, to step through this lab
Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
Note: If you see the Choose an account dialog, click Use Another Account.
If necessary, copy the Username below and paste it into the Sign in dialog.
student-02-8f3000ee4a36@qwiklabs.net
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You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
mvN6WYw0u4DH
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You can also find the Password in the Lab Details pane.
Click Next.
Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials.
Note: Using your own Google Cloud account for this lab may incur extra charges.
Click through the subsequent pages:
Accept the terms and conditions.
Do not add recovery options or two-factor authentication (because this is a temporary account).
Do not sign up for free trials.
After a few moments, the Google Cloud console opens in this tab.
Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.
Task 1. Open the notebook in Vertex AI Workbench
In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.
Find the vertex-ai-jupyterlab instance and click on the Open JupyterLab button.
The JupyterLab interface for your Workbench instance opens in a new browser tab.
Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:
1. Close the browser tab for JupyterLab, and return to the Workbench home page.
2. Select the checkbox next to the instance name, and click Reset.
3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.
Task 2. Set up the notebook
Open the intro_prompt_design file.
In the Select Kernel dialog, choose Python 3 from the list of available kernels.
Run through the Getting Started and the Import libraries sections of the notebook.
- For Project ID, use
qwiklabs-gcp-01-c384390b2792, and for Location, use europe-west4.
Note: You can skip any notebook cells that are noted Colab only. If you experience a 429 response from any of the notebook cell executions, wait 1 minute before running the cell again to proceed.
Click Check my progress to verify the objective.
Install packages and import libraries
Task 3. Prompt engineering best practices
Prompt engineering is all about how to design your prompts so that the response is what you were indeed hoping to see. The idea of using "unfancy" prompts is to minimize the noise in your prompt to reduce the possibility of the LLM misinterpreting the intent of the prompt. Below are a few guidelines on how to engineer "unfancy" prompts.
In this section, you'll cover the following best practices when engineering prompts:
Be concise
Be specific, and well-defined
Ask one task at a time
Improve response quality by including examples
Turn generative tasks to classification tasks to improve safety
- Run through the Be concise section of the notebook.
Click Check my progress to verify the objective.
Be concise
- Run through the Be specific, and well-defined section of the notebook.
Click Check my progress to verify the objective.
Be specific, and well-defined
- Run through the Ask one task at a time section of the notebook.
Click Check my progress to verify the objective.
Ask one task at a time
- Run through the Watch out for hallucinations section of the notebook.
Click Check my progress to verify the objective.
Watch out for hallucinations
Task 4. Reduce Output Variability
How can you attempt to reduce the chances of irrelevant responses and hallucinations? One way is to provide the LLM with system instructions. In this section, you will see how system instructions works and how you can use them to reduce hallucinations or irrelevant questions for a travel chatbot.
- Run through the Using system instructions to guardrail the model from irrelevant responses section of the notebook.
Click Check my progress to verify the objective.
Using system instructions to guardrail the model from irrelevant responses
- Run through the Turn generative tasks into classification tasks to reduce output variability section of the notebook.
Click Check my progress to verify the objective.
Generative tasks lead to higher output variability
- Run through the Classification tasks reduces output variability section of the notebook.
Click Check my progress to verify the objective.
Classification tasks reduces output variability
Task 5. Improve Response Quality by Including Examples
Another way to improve response quality is to add examples in your prompt. The LLM learns in-context from the examples on how to respond. Typically, one to five examples (shots) are enough to improve the quality of responses. Including too many examples can cause the model to over-fit the data and reduce the quality of responses.
Similar to classical model training, the quality and distribution of the examples is very important. Pick examples that are representative of the scenarios that you need the model to learn, and keep the distribution of the examples (e.g. number of examples per class in the case of classification) aligned with your actual distribution.
- Run through the Improve response quality by including examples section of the notebook.
Click Check my progress to verify the objective.
Improve response quality by including examples
Solution of Lab
💡
When I open JupyterLab, I can't see any files. What is the problem? You must be on the Workbench home page, select the checkbox next to the instance name, click Reset, and then the Open JupyterLab button is enabled again.
Open link: https://console.cloud.google.com/vertex-ai/workbench
press Shift+Enter
