How to Build No-Code AI Solutions on AWS with Amazon Bedrock

Generative AI is changing the way businesses work, interact with their customers, and drive innovation. If you’re starting to build a generative AI-powered solution, you might be wondering how to deal with the complexities involved, from choosing the right model to managing prompts and ensuring data privacy.
In this article, we will guide you through the process of building a generative AI application using Amazon Web Services (AWS), focusing on Amazon Bedrock.This resource is for both experienced AI engineers and those new to generative AI, explaining how to maximize the capabilities of Amazon Bedrock during your journey.
Amazon Bedrock is a fully managed service that provides a unified API that gives access to a wide range of top-level foundation models (FMs) from well-known AI companies such as Anthropic, Cohere, Meta, Mistral AI, AI21 Labs, Stability AI, and Amazon itself. It comes equipped with a comprehensive set of tools and features geared towards efficiently building generative AI applications while following best practices regarding security, privacy, and responsible AI.
Integrating a generative AI feature into your application can be simplified through a single-turn interaction with a large language model (LLM). Whether you need to generate text, answer questions, or summarize information based on user input, Amazon Bedrock streamlines the development and scaling of generative AI applications with a unified API.By supporting both the Amazon model and models from other leading AI providers, it gives you the flexibility to explore different options without having to commit to any one model or provider.Since AI is evolving rapidly, you can easily change models for better performance without rewriting your application.
Amazon Bedrock also broadens your horizons with the Amazon Bedrock Marketplace, where you can access over 100 exclusive FMs.This marketplace allows you to explore, test, and integrate new capabilities through fully managed endpoints.Whether you’re looking for the latest advancements in text generation, image synthesis, or any domain-specific AI, Amazon Bedrock gives you the adaptability you need to seamlessly scale your solutions.
With a single API, you can maintain flexibility by easily switching between models, upgrading to the latest versions, and future-proofing your generative AI applications with very few code changes required. In summary, here are some of the key benefits of Amazon Bedrock:

• Simplicity: Eliminate the hassle of infrastructure management or handling multiple APIs.
• Flexibility: Experiment with various models to identify the best fit for your needs.
• Scalability: Scale your application effortlessly without concerns about the underlying resources.

After integrating the basic LLM feature, the next step is to optimize performance and make sure you are using the right model for your needs. This brings us to the importance of evaluating and comparing models.

Choosing the right model for your needs

Finding the ideal model for your specific application is essential. With the wide range of options, determining which model will provide optimal performance can be a challenge.Whether your goal is to generate relevant answers, summarize data, or address complex queries, selecting the right model is imperative to achieving the best results.
You can leverage Amazon Bedrock’s model evaluation tools to systematically test different models and find out which model is better for your particular scenario.Whether you are beginning development of your project or are in the final stages before launch, the choice of model can dramatically impact the efficacy of your generative AI solutions.

The evaluation process involves several key elements:

1. Automated and Human Evaluation: Start by testing different models through automated metrics such as accuracy, robustness or toxicity.Additionally, involving human evaluators can help assess more nuanced aspects including friendliness, style, and alignment with your brand voice.
2. Custom Datasets and Metrics: Measure model performance using your own datasets or established options.Customize evaluation metrics to focus on what’s most important for your project, ensuring the chosen model aligns with your business or operational objectives.
3. Iterative Feedback: Evaluate iteratively during development to refine your options more quickly. This side-by-side comparison enables you to make informed, data-driven decisions when selecting the model that best suits your needs. For example, if you are developing a customer support AI assistant for an e-commerce platform.You can evaluate multiple models using real customer questions to see which model provides the most accurate, friendly, and relevant responses.Comparing these models can help you choose the model that promises the best user experience.

Once you have evaluated and selected the ideal model, the next step is to ensure it meets your business needs.While off-the-shelf models can perform well enough, additional customization is required to achieve a truly personalized experience.This leads to a crucial step in your generative AI journey: tailoring the model to fit your unique business context. It is important to ensure that the model generates accurate and contextually appropriate responses.Even top-performing models may not have access to the latest or industry-specific information that’s critical to your operations.To solve this problem, the model should use data sources that you own, and ensure that its outputs are current and relevant.This is where Retrieval Augmented Generation (RAG) comes into play, enriching the model’s responses by integrating your organization’s unique knowledge base.

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Enriching model responses with your proprietary data

Publicly available big language models (LLMs) may excel at common knowledge tasks, but they can fall short when it comes to outdated information or when it lacks context related to your organization’s proprietary data.To ensure that the model gets access to the most relevant and up-to-date information, you need a strategy that increases its accuracy and contextual awareness. Here are two effective approaches to enrich the model's responses:
1.RAG (Retrieval-Augmented Generation): This method allows you to dynamically pull relevant information in real-time during a query, enhancing model responses without the need for retraining.
2.Fine-tuning: In this approach, you can personalize your chosen model by training it on proprietary data. This helps improve its ability to deal with organization-specific tasks or specialized knowledge.

It is advisable to start with RAG because it is flexible and easy to implement. If needed, you can fine-tune the model to be more adaptive to your domain.RAG works by retrieving contextual information at query time, ensuring that the model's responses are accurate and context-aware. Initially, the data is processed and indexed into a vector database or comparable retrieval system.When a user submits a question, Amazon Bedrock combs through this indexed data to find the source of the relevant context, which is then added to the prompt.The model generates answers based on both the original question and the insights gained, with no additional training required.

Additionally, Amazon Bedrock Knowledge Base automates the RAG pipeline – including aspects such as data ingestion, retrieval, prompt escalation, and quote creation – which simplifies the process of setting up custom integrations.By smoothly incorporating proprietary data, you ensure that models generate accurate, contextually rich, and constantly updated responses. The Bedrock Knowledge Base accommodates a variety of data types, allowing you to tailor AI-generated responses to your business-specific needs:

• Unstructured Data: Extract information from text-heavy resources such as documents, PDFs, and emails.
• Structured Data: Enable natural language queries across databases, data lakes, and data warehouses without the overhead of moving or preprocessing the data.
• Multimodal Data: Process both text and visual components in documents and images through Amazon Bedrock Data Automation.
• GraphRAG: Enhance knowledge retrieval with graph-based relationships, enabling AI to understand entity connections for more context-sensitive responses.

With these capabilities, Amazon Bedrock reduces data silos, and streamlines the enhancement of AI applications with both real-time and historical knowledge.Whether working with text, images, structured datasets, or interconnected knowledge graphs, Amazon Bedrock provides a fully managed, scalable solution that eliminates the need for complex infrastructure.
In conclusion, leveraging RAG with Amazon Bedrock offers several benefits:

• Up-to-date information: Answers are incorporated with the latest data from your knowledge base.
• Accuracy: Reduces the chance of erroneous or irrelevant answers.
• No additional infrastructure: Avoid the hassle of setting up and managing your own vector database or custom integrations.

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Customizing Models to Fit Your Business Needs

While common base models (FMs) provide a solid baseline, they often miss the target in terms of accuracy, brand voice, and specific industry knowledge needed for real-world use.Perhaps the language isn’t in sync with your brand, or the model falls short on particular words. You may have tried prompt engineering and retrieval-augmented generation (RAG) to provide additional context for better responses.Although these approaches are helpful, they also have limitations – such as long prompts can slow response times and increase costs – and they still cannot provide the deep expertise needed for specific tasks.To truly leverage generative AI, businesses need a secure way to optimize models so that AI-generated responses are accurate, relevant, reliable, and in line with their goals.
Amazon Bedrock simplifies model customization, allowing businesses to enhance foundational models with their proprietary data – without the hassle of building a model from scratch or dealing with complex infrastructures.

Rather than retraining the entire model, Amazon Bedrock provides a fully managed fine-tuning process that creates a private copy of the base FM.This ensures that your proprietary data remains confidential and is not used to train the original model. Amazon Bedrock provides two effective ways to efficiently refine models:
1.Fine-tuning: You can improve FM by using labeled datasets to increase accuracy across industry-relevant terminology, brand voice, and internal workflows. This enables the model to give more accurate, context-aware responses without the need for complex cues.
2.Continuous Pre-training: If you have unlabeled data specific to your domain, this method allows for additional training of the FM on the specific knowledge without the need for manual labeling.This is especially beneficial for regulatory compliance, domain-specific languages, or evolving business practices.

By combining fine-tuning for core industry expertise with RAG for real-time knowledge retrieval, businesses can develop highly specialized AI models that remain accurate and adaptable while ensuring response styles align with their objectives. In summary, Amazon Bedrock delivers several key advantages:

• Privacy-Preserved Customization:Fine-tune models securely while guaranteeing your proprietary data remains private.
• Efficiency: Attain high accuracy and domain relevance without the intricacies of building models from the ground up.

As your project progresses, managing and optimizing prompts becomes essential, especially when dealing with various iterations or testing different versions. The next step is to refine your prompts to maximize the model’s performance.

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Content Source: https://aws.amazon.com/blogs/machine-learning/build-generative-ai-solutions-with-amazon-bedrock/