Large language models (LLMs) are trained on extensive datasets to understand and generate human-like text. LLMs have advanced significantly in recent years, resulting in widespread adoption in a variety of industries, including customer service, content generation and healthcare, as well as significant improvements in natural language understanding and generation.
Building generative AI (genAI) applications powered by LLMs for production is a complex endeavor that requires careful planning and execution. As these models continue to advance, their integration into real-world applications brings both opportunities and challenges. Key considerations include selecting the best-suited LLM for specific tasks, ensuring reliable performance through rigorous evaluation, getting access to powerful tools and capabilities that will help build apps on these models, mitigating risks such as hallucinations and managing model responses effectively.
Amazon Bedrock is a fully managed service from Amazon Web Services (AWS), which announced several new features for Bedrock in July at AWS Summit NY. It empowers developers to build and scale genAI applications with ease. As a fully managed service, Amazon Bedrock abstracts away the need for managing infrastructure, enabling developers to focus on building applications. It also provides seamless scalability to handle varying workloads.
This guide aims to highlight the key challenges in developing genAI applications, as well as how Amazon Bedrock addresses them, to help developers be more productive and efficient.
Choose the Right Model for Your Use Case
When developing genAI applications for production, it’s crucial to recognize that no single model fits all needs, as the choice of LLM significantly impacts the application’s performance, scalability and suitability for specific tasks. Different LLMs excel in various areas, with capabilities varying widely based on factors such as model size, training data, cost and underlying architecture. For instance, some LLMs may be better suited for tasks requiring a deep understanding of context and nuance, while others may be better suited for image processing and generation.
Experimenting with multiple LLMs and performing thorough model evaluations using representative data and test cases helps ensure an application remains effective and competitive. This approach allows for informed decisions based on empirical evidence rather than theoretical capabilities or marketing claims. As the field evolves rapidly, staying up to date with the latest developments and periodically reevaluating your choice is essential.
Amazon Bedrock provides access to a wide range of high-performing foundation models from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI and Amazon’s own Titan models. The platform also provides a powerful model evaluation capability that allows customers to evaluate, compare and select the best foundation model for their specific use case and requirements. Model evaluation streamlines the often time-consuming process of benchmarking and choosing the right model, reducing the time from weeks to just hours. This allows customers to quickly identify the best model fit and bring new genAI applications to market faster.
Build Models With Your Data and Custom Model Import
While pre-trained LLMs like GPT-3 and BERT have achieved remarkable performance across a wide range of natural language tasks, they are often trained on broad, general-purpose datasets. As a result, these models may not perform optimally when applied to specific domains or use cases that deviate significantly from their training data. This is where the need for customizing models arises.
Customizing pre-trained models involves fine-tuning them on domain-specific data, allowing the models to adapt and specialize for the unique characteristics, terminology and nuances of a particular industry, organization or application. By using customized models, businesses can unlock several key benefits.
Amazon Bedrock allows organizations to customize foundation models with their own proprietary data to build applications tailored to specific domains, organizations and use cases. This process, known as data gravity, enables customers to create unique user experiences that reflect their company’s style, voice and services.
There are two main methods for model customization in Bedrock: fine-tuning and continued pretraining. Fine-tuning involves providing a labeled training dataset to specialize the model for specific tasks. By learning from annotated examples, the model’s parameters are adjusted to associate the right outputs with corresponding inputs, improving its performance on the tasks represented in the training data.
Continued pretraining, on the other hand, utilizes unlabeled data to expose the model to certain input types and domains. By training on raw data from industry or business documents, the model accumulates robust knowledge and adaptability beyond its original training, becoming more domain-specific and attuned to that domain’s terminology.
In addition to fine-tuning and continued pre-training, Amazon Bedrock now offers Custom Model Import, allowing customers to use prior model customization investments within Bedrock’s fully managed environment. With this new feature, developers can easily import models customized outside of Bedrock, such as those fine-tuned or adapted using Amazon SageMaker or other third-party tools, and access them on-demand through Bedrock’s invoke model API.
Customize LLM Responses Based on Business Needs
Integrating the LLM with a retrieval system that searches through databases or document collections to find relevant context before producing a response can result in grounding. The retrieved information acts as an additional input, guiding the model to produce outputs consistent with the grounding data. This approach has been shown to significantly improve factual accuracy and reduce hallucinations, especially for open-ended queries where models are more prone to hallucinate.
LLM hallucination occurs when LLMs generate responses that are incorrect, nonsensical or completely fabricated without being based on factual data. It occurs due to limitations in the training data, biases or the model’s inability to distinguish between plausible and factual outputs.
One effective approach to mitigating hallucinations in LLMs is to ground them in external data sources and knowledge bases during inference. This technique, known as grounding or retrieval-augmented generation (RAG), involves incorporating relevant information from trusted sources into the model’s generation process. Instead of relying solely on the patterns learned during pretraining, grounded models can access and condition on factual knowledge, reducing the likelihood of generating plausible but false statements.
Amazon Bedrock Knowledge Bases is a fully managed service that lets developers use RAG workflows to add relevant information from their company’s data sources to the answers given by LLMs. It streamlines the entire RAG process, from ingesting data from Amazon S3 to converting it into embeddings using foundation models, storing the embeddings in a vector database, and retrieving and augmenting prompts with relevant information at query time.
Integrate External Systems and Data Sources to Build AI Agents
Connecting LLMs to external systems and tools enables them to access current information, execute complex, multistep actions and overcome the inherent limitations of relying solely on training data. Integrating LLMs with external data sources, tools and systems is critical to realizing their full potential in production. This integration provides access to up-to-date, domain-specific information, enhancing accuracy, relevance and functionality.
Agents are advanced AI systems that use the capabilities of LLMs to exhibit autonomous behavior and perform complex tasks beyond just text generation. They play a crucial role in leveraging LLMs’ full potential. Agents are specialized components designed to handle specific tasks by interacting with both the LLM and external systems. They can orchestrate complex workflows, automate repetitive tasks and help ensure that the LLM’s outputs are actionable and relevant. By using agents, developers can build applications that not only understand and generate language but also perform real-world actions, bridging the gap between language processing and practical application.
Amazon Bedrock agents are advanced AI systems that combine LLMs with the ability to interact with external data sources, APIs and tools. They enable developers to build autonomous agents that can understand natural language instructions, orchestrate complex multistep workflows and take actions beyond just generating text responses.
Bedrock agents work by first parsing the user’s natural language input using a foundation model. Based on the instructions provided during agent creation, the agent then determines the appropriate course of action, such as retrieving relevant information from a knowledge base, invoking external APIs or tools or breaking down the request into smaller subtasks. The agent can iteratively refine its understanding, gather additional context from various sources and ultimately provide a final response synthesized from multiple inputs.
Safeguard LLM Responses to Build AI Responsibly
Prompt engineering is an effective approach to guiding the LLM’s generation process. Crafting specific prompts can set the tone, context and boundaries for desired outputs, leading to the implementation of responsible AI. While prompt engineering defines the input and expected output of LLMs, it might not have complete control over the responses delivered to end users. This is where guardrails come into play.
Implementing effective guardrails requires a multifaceted approach involving continuous monitoring, evaluation and iterative improvements. Guardrails must be tailored to each LLM-based application’s unique requirements and use cases, considering factors like target audience, domain and potential risks. They contribute to ensuring that outputs are consistent with desired behaviors, adhere to ethical and legal standards, and mitigate risks or harmful content. Controlling and managing model responses through guardrails is crucial for building LLM-based applications.
Within these guardrails, content filters and moderation systems are vital for detecting and filtering harmful, offensive or biased language. These systems can be implemented at various stages of the generation process. Controlled generation techniques, such as top-k or top-p sampling, limit the model’s output to the most probable or relevant tokens, improving coherence and relevance.
Amazon Bedrock Guardrails is a data governance feature that allows developers to implement safeguards and governance policies for their genAI applications. It provides a way to customize the behavior of foundation models and help ensure they adhere to their organization’s responsible AI policies. Guardrails works by evaluating the inputs to and outputs from the FMs against the defined policies. With Guardrails, developers can define rules to filter out harmful content, block denied topics, redact sensitive information like PII and enforce content moderation based on their requirements.
Ensure Security and Privacy in LLM-Based Applications
Building LLM-based applications involves unique security and privacy challenges. These applications often handle vast amounts of data, some of which can be sensitive or proprietary. Key considerations include the risk of data breaches, which can lead to significant privacy infringements and intellectual property theft, making data protection through encryption and access controls paramount.
Another major concern is model manipulation, where adversaries might attempt to manipulate LLM outputs, leading to biased or harmful results. Additionally, infrastructure vulnerabilities must be addressed to secure the hardware and networks supporting LLMs, ensuring operational integrity. Ethical and legal risks are also significant, requiring LLMs to comply with standards and regulations to avoid generating biased content or infringing on intellectual property rights.
Amazon Bedrock security and compliance incorporates multiple strategies to address the security and privacy concerns inherent in LLM-based applications. It employs industry-standard encryption protocols to protect data in transit and at rest and uses stringent access control mechanisms like role-based access control (RBAC) so that only authorized personnel can access sensitive data and functionalities.
By adhering to various compliance standards such as GDPR and HIPAA, Amazon Bedrock’s data handling practices meet regulatory requirements, while comprehensive logging and auditing capabilities allow continuous monitoring and tracking of all interactions, ensuring transparency and accountability.
Secure API integrations and privacy-preserving techniques are utilized within Amazon Bedrock to prevent data leakage during interactions with external systems and APIs. Finally, Amazon Bedrock has a robust incident response framework that includes regular security assessments and threat modeling. It also implements proactive measures like rate limiting, logging and alerting mechanisms to prevent over-reliance on LLMs and enable accurate and secure model outputs.
Summary
Building generative AI applications powered by LLMs requires meticulous planning and execution to ensure high performance, security and ethical standards.
Amazon Bedrock, a fully managed service from Amazon Web Services, facilitates the development and scaling of these applications by abstracting infrastructure management, thus allowing developers to concentrate on application building. Key considerations include selecting the appropriate LLM for specific tasks, employing prompt engineering, implementing robust guardrails and grounding models to reduce hallucinations. Additionally, integrating LLMs with external systems and ensuring stringent security and privacy measures are crucial.
By using Amazon Bedrock’s comprehensive suite of tools and capabilities, developers can build efficient, reliable and responsible genAI applications, ultimately bringing innovative solutions to market more swiftly.
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Use these guidelines to build robust, reliable generative AI applications that meet a high bar for performance, security and ethical standards.