Architecting Your Organization’s Generative AI Journey: A Systematic Approach with Amazon Bedrock
Organizations today face the challenge of keeping up with the pace of change in generative AI capabilities while ensuring their adoption follows a structured, value-driven approach. There is a need to build a modular foundational capability that enables to move beyond POCs to scalable, sustainable and measurable business value.
From text-based pioneers to multimodal (any to any) architects, Gen AI models now ascend as reasoning engines — Enterprises requires seamless access to these models to maximize innovation
Every Gen AI application starts with Foundation Models. We have seen an exponential progression in Model Intelligence in the last one year. From text-based models, multi modal models, any to any models, we are entering in to the reasoning era. However, each of these models have their place in building generative AI applications. Organizations require an marketplace to access these models on demand and systematic approach to evaluating models for the use case.
Amazon Bedrock provides a model catalog with access to over 180 models ranging from 1st party models (Amazon Nova), Partner models (Anthropic, Cohere, AI21) to Open source / open weights models (Meta, Mistral and much more). Models are accessible
Enterprise-ready accessibility of models is key to drive ROI — High Accuracy, Low cost, Low latency & Higher throughput
Organizations must prioritize models that deliver not just high accuracy, but demonstrate optimal performance across operational parameters — cost, latency, and throughput. When these operational parameters are effectively balanced, organizations can achieve sustainable ROI from their Generative AI initiatives.
Amazon Bedrock offers performance optimization options for reduced cost, reduced latency and increased throughput.
For more information on leveraging these options for your use case, refere to this blog — https://medium.com/@meenakshisundaram-t/amazon-bedrock-inference-options-d46d668b8ef0
Hyper personalization — the silent differentiator that can elevate your Gen AI products to market leaders. And integrating your data landscape with the Models fuels hyper-personalization
Your data is your key differentiator. Integrating your data with the Gen AI application enables to build market leading differentiated offerings. You could fine-tune a model to build a domain trained model or use RAG based approaches. In this blog we will just focus on RAG based applications. Categorically this data can be divided in to 1/ situational data and 2/ Semantic data.
Situational data often refers to the information regarding “who is asking the question” and “why is the person asking the question”. An understanding of this enables the Gen AI application to provide a personalized and pointed answer.
For example. A person asking information to book tickets — I’m planning a two-week trip to Japan in April with my family. Can you suggest an itinerary”.
Situational data in this case refers to information you would have collected and stored in your Systems of Record — information about the person (location, language, budget…) and Past trips and activities (showing preferences on travel, loading, attractions visited)
Semantic data on the other hand refers to the domain intelligence available in structured and unstructured format. In the above example, this could be hidden gems / ranking of attractions / fast track tickets collated through data and insights developed within the organization.
With Amazon Bedrock you can connect to your situational data stored in your system of record leveraging a Lambda function. Amazon Bedrock provides Bedrock Knowledge Base — to effectively build your Semantic knowledge from structured and unstructured data. Bedrock Knowledge base leverages battle tested services in OpenSearch Serverless, Aurora Postgres PgVector and trusted partner databases in Mongo DB, PineCone, Redis.
Responsible Gen AI practices is critical for enterprises to mitigate compliance risks, protect sensitive employee data, and uphold ethical standards — safeguarding trust while fostering innovation
With access to models, ability to invoke models based on your use case NFR needs (low cost, low latency, higher throughput) and integrating your data in to your Gen AI application, you will be able to build Gen AI application. However the sheer nature of Gen AI is non-deterministic, prone to hallucinations, prompt leaks, prompt injection and much more. To address this you will have to implement guardrails.
Amazon Bedrock guardrails provides 3 layers of safeguards — prompts and responses safeguards, contextually grounding your response and mathematical validation of your response using Automated reasoning
To learn more about how to implement guardrails, refer to this blog — https://medium.com/@meenakshisundaram-t/responsible-ai-lens-gen-ai-poc-to-prod-part-1-0e7efde9da83
Agentic systems will enable Enterprises to decompose complex workflows into modular, AI-driven processes that automate tasks, personalize employee interactions, and unify data insights
Next step is to elevate your Gen AI applications to in to Agentic system say an Agentic RAG system for example or implementing actions using tools. This requires for you to build an AI agent.
Amazon Bedrock Agents support single agent and multi agent systems bringing together Models, Data, Tools and Guardrails
To learn about what an agent is and how to build Bedrock Agents, refer to this blog here — https://medium.com/@meenakshisundaram-t/decoding-amazon-bedrock-agents-375c6167accc
Time-to-market isn’t just a metric, it’s your competitive edge — Scaling your GenAI application from proof-of-concept to production faster is key for an enterprise.
Integrating the above capabilities from Agents, guardrails, knowledge bases, models consumes time, increases operational overhead in production. Amazon Bedrock Flows accelerates the creation, testing, and deployment of user-defined workflows for generative AI applications through an intuitive visual builder. You can seamlessly drag, drop and link Prompts, Agents, Knowledge bases, Guardrails, Lex, Lambda, other AWS services, with business logic to create a workflow. This removes the need to write code and offers easy customization of the business logic.
Closing thoughts: Amazon Bedrock is a generative AI platform that provides capabilities and options within the generative AI ecosystem to build secure, modular, serverless, and production-grade applications
Amazon Bedrock is a fully managed Generative AI Service providing you with all the capabilities (models, guardrails, tools, knowledge bases, agents etc..) required to build diverse Gen AI Applications.
To learn more about Amazon Bedrock capabilities refer to the blog here — https://medium.com/@meenakshisundaram-t/amazon-bedrock-an-enterprise-generative-ai-platform-f8d62c814829
