Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

With the advent of generative AI solutions, organizations are finding different ways to apply these technologies to gain edge over their competitors. Intelligent applications, powered by advanced foundation models (FMs) trained on huge datasets, can now understand natural language, interpret meaning and intent, and generate contextually relevant and human-like responses. This is fueling innovation across industries, with generative AI demonstrating immense potential to enhance countless business processes, including the following: Accelerate research and development through automated hypothesis generation and experiment design Uncover hidden insights by identifying subtle trends and patterns in data Automate time-consuming documentation processes Provide better customer experience with personalization Summarize data from various knowledge sources Boost employee productivity by providing software code recommendations Amazon Bedrock is a fully managed service that makes it straightforward to build and scale generative AI applications. Amazon Bedrock offers a choice of high-performing foundation models from leading AI companies, including AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon, via a single API. It enables you to privately customize the FMs with your data using techniques such as fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG), and build agents that run tasks using your enterprise systems and data sources while complying with security and privacy requirements. In this post, we discuss how to use the comprehensive capabilities of Amazon Bedrock to perform complex business tasks and improve the customer experience by providing personalization using the data stored in a database like Amazon Redshift. We use prompt engineering techniques to develop and optimize the prompts with the data that is stored in a Redshift database to efficiently use the foundation models. We build a personalized generative AI travel itinerary planner as part of this example and demonstrate how we can personalize a travel itinerary for a user based on their booking and user profile data stored in Amazon Redshift. Prompt engineering Prompt engineering is the process where you can create and design user inputs that can guide generative AI solutions to generate desired outputs. You can choose the most appropriate phrases, formats, words, and symbols that guide the foundation models and in turn the generative AI applications to interact with the users more meaningfully. You can use creativity and trial-and-error methods to create a collection on input prompts, so the application works as expected. Prompt engineering makes generative AI applications more efficient and effective. You can encapsulate open-ended user input inside a prompt before passing it to the FMs. For example, a user may enter an incomplete problem statement like, “Where to purchase a shirt.” Internally, the application’s code uses an engineered prompt that says, “You are a sales assistant for a clothing company. A user, based in Alabama, United States, is asking you where to purchase a shirt. Respond with the three nearest store locations that currently stock a shirt.” The foundation model then generates more relevant and accurate information. The prompt engineering field is evolving constantly and needs creative expression and natural language skills to tune the prompts and obtain the desired output from FMs. A prompt can contain any of the following elements: Instruction – A specific task or instruction you want the model to perform Context – External information or additional context that can steer the model to better responses Input data – The input or question that you want to find a response for Output indicator – The type or format of the output You can use prompt engineering for various enterprise use cases across different industry segments, such as the following: Banking and finance – Prompt engineering empowers language models to generate forecasts, conduct sentiment analysis, assess risks, formulate investment strategies, generate financial reports, and ensure regulatory compliance. For example, you can use large language models (LLMs) for a financial forecast by providing data and market indicators as prompts. Healthcare and life sciences – Prompt engineering can help medical professionals optimize AI systems to aid in decision-making processes, such as diagnosis, treatment selection, or risk assessment. You can also engineer prompts to facilitate administrative tasks, such as patient scheduling, record keeping, or billing, thereby increasing efficiency. Retail – Prompt engineering can help retailers implement chatbots to address common customer requests like queries about order status, returns, payments, and more, using natural language interactions. This can increase customer satisfaction and also allow human customer service teams to dedicate their expertise to intricate and sensitive customer issues. In the following example, we implement a use case from the travel and hospitality industry to implement a personalized travel itinerary planner for customers who have upcoming travel plans. We demonstrate how we can build a generative AI chatbot that interacts with users by enriching the prompts from the user profile data that is stored in the Redshift database. We then send this enriched prompt to an LLM, specifically, Anthropic’s Claude on Amazon Bedrock, to obtain a customized travel plan. Amazon Redshift has announced a feature called Amazon Redshift ML that makes it straightforward for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Redshift data warehouses. However, this post uses LLMs hosted on Amazon Bedrock to demonstrate general prompt engineering techniques and its benefits. Solution overview We all have searched the internet for things to do in a certain place during or before we go on a vacation. In this solution, we demonstrate how we can generate a custom, personalized travel itinerary that users can reference, which will be generated based on their hobbies, interests, favorite foods, and more. The solution uses their booking data to look up the cities they are going to, along with the travel dates, and comes up with a precise, personalized list of things to do. This solution can be used by the travel and hospitality industry to embed a personalized travel itinerary planner within their travel booking portal. This solution contains two major components. First, we extract the user’s information like name, location, hobbies, interests, and favorite food, along with their upcoming travel booking details. With this information, we stitch a user prompt together and pass it to Anthropic’s Claude on Amazon Bedrock to obtain a personalized travel itinerary. The following diagram provides a high-level overview of the workflow and the components involved in this architecture. First, the user logs in to the chatbot application, which is hosted behind an Application Load Balancer and authenticated using Amazon Cognito. We obtain the user ID from the user using the chatbot interface, which is sent to the prompt engineering module. The user’s information like name, location, hobbies, interests, and favorite food is extracted from the Redshift database along with their upcoming travel booking details like travel city, check-in date, and check-out date. Prerequisites Before you deploy this solution, make sure you have the following prerequisites set up: Deploy this solution Use the following steps to deploy this solution in your environment. The code used in this solution is available in the GitHub repo. The first step is to make sure the account and the AWS Region where the solution is being deployed have access to Amazon Bedrock base models. On the Amazon Bedrock console, choose Model access in the navigation pane. Choose Manage model access. Select the Anthropic Claude model, then choose Save changes. It may take a few minutes for the access status to change to Access granted. Next, we use the following AWS CloudFormation template to deploy an Amazon Redshift Serverless cluster along with all the related components, including the Amazon Elastic Compute Cloud (Amazon EC2) instance to host the webapp. Choose Launch Stack to launch the CloudFormation stack: Provide a stack name and SSH keypair, then create the stack. On the stack’s Outputs tab, save the values for the Redshift database workgroup name, secret ARN, URL, and Amazon Redshift service role ARN. Now you’re ready to connect to the EC2 instance using SSH. Open an SSH client. Locate your private key file that was entered while launching the CloudFormation stack. Change the permissions of the private key file to 400 (chmod 400 id_rsa). Connect to the instance using its public DNS or IP address. For example: ssh -i “id_rsa” ec2-user@ ec2-54-xxx-xxx-187.compute-1.amazonaws.com Update the configuration file personalized-travel-itinerary-planner/core/data_feed_config.ini with the Region, workgroup name, and secret ARN that you saved earlier. Run the following command to create the database objects that contain the user information and travel booking data: python3 ~/personalized-travel-itinerary-planner/core/redshift_ddl.py This command creates the travel schema…

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