In the first part of our series, Introduction to Retrieval Augmented Generation, we introduced the fundamentals of Retrieval Augmented Generation (RAG), highlighting how it blends retrieval and generation to produce contextually rich and mission-aligned responses. In this post, we dive into a practical implementation scenario for the federal landscape, integrating AWS Bedrock’s Large Language Model (LLM) capabilities, Elastic Cloud’s indexing and semantic search, and continuous web crawling.
For federal agencies, the promise of RAG isn’t just about providing answers, it’s about delivering OptimalAnswers—accurate, compliant, and transparent responses where grounding, relevancy, and guardrails play critical roles. By designing architectures that emphasize these principles, agencies can confidently utilize AI to meet strict regulatory standards while improving the search user experience.
Bottom Line Up Front:
Cost Savings: By using cloud-managed services like AWS Bedrock and Elastic Cloud, agencies can save up to 30% to 50% in total cost of ownership compared to self-hosted or on-prem platform management. These managed services reduce infrastructure expenses and eliminate the need for significant maintenance efforts.
Minimal Maintenance: Managed services require little oversight, enabling federal IT teams to focus on delivering mission-critical solutions rather than maintaining complex AI infrastructure.
Security: All data remains within the federal cloud enclave, ensuring that proprietary and sensitive information is not exposed to the public or used for LLM training. This guarantees compliance with federal security standards and protects agency data.
Improved Accuracy: Grounding AI responses in authoritative, up-to-date data ensures compliance and trustworthiness, aligning with executive orders and federal guidelines.
Efficiency: Modular, adaptable architectures streamline updates and minimize downtime.
Revisiting Retrieval Augmented Generation
RAG enriches an LLM’s responses with up-to-the-minute, authoritative data. Instead of relying solely on a model’s pre-trained internal knowledge, RAG integrates a retrieval step to access external documents. These documents are then “augmented” into the prompt, ensuring the final answer is informed by relevant, contextually correct content. For federal IT leaders, this approach aligns perfectly with the need for trustworthy, policy-driven insights that reduce guesswork and prevent misinformation.
The newly issued Executive Order on Removing Barriers to American Leadership in Artificial Intelligence (2025) emphasizes the need for unbiased, innovation-driven AI development. This shift moves away from prior risk-mitigation frameworks toward fostering American competitiveness and leadership in AI. A key element of this approach is ensuring that AI systems remain efficient, transparent, and rooted in reliable data while removing unnecessary regulatory barriers. RAG plays a vital role in supporting these objectives by enhancing the accuracy and trustworthiness of AI-driven systems while ensuring they operate free from engineered bias.
Why Relevancy and Trustworthiness Matters
Traditional keyword searches often overwhelm users with long lists of results, demanding significant effort for interpretation. Relevancy, ensuring returned documents closely match user intent, is critical, especially in federal settings where decisions rely on accurate information. Vector-based semantic search, as employed by systems like Elastic Cloud, enhances relevancy by identifying documents based on conceptual meaning rather than just keywords, leading to clearer and more actionable answers.
Grounding Responses in Authoritative Content and Aligning with Executive Orders
One of the primary concerns with generative AI is “hallucination,” where the model generates plausible but incorrect information. This poses a significant risk for federal agencies, potentially undermining trust and leading to flawed decisions. RAG directly addresses this risk by grounding responses in authoritative sources, directly supporting the executive orders’ focus on trustworthy AI. This is achieved by retrieving relevant information from validated external knowledge bases, agency websites, and other official documents and incorporating this information into the LLM’s prompt. This process ensures that every answer is traceable back to verifiable sources, fostering accountability and building confidence in the information provided.
Promoting Trustworthy AI
RAG directly contributes to this goal by leveraging validated knowledge bases, mitigating misinformation and hallucinations, and ensuring responses are grounded in official documents and regulations. For instance, a federal agency using RAG to answer public policy queries ensures responses are based on official documents, promoting factual accuracy.
Ensuring Safety, Security, and Compliance
RAG systems can be designed to adhere to privacy laws, civil rights protections, and security requirements by integrating relevant datasets and policies. The inherent auditability of RAG, through source identification, further enhances compliance and monitoring capabilities, particularly crucial in sensitive applications like national security.
Enhancing Transparency
By explicitly surfacing the sources used to generate content, RAG promotes transparency and understandability, a core principle of both executive orders. Displaying document excerpts or links alongside responses builds user confidence in the system’s reliability.
Guardrails, Compliance, and Further Alignment with Executive Orders
Platforms like AWS Bedrock offer guardrails that enable agencies to maintain control and oversight over generated content. These guardrails, combined with careful prompt engineering, help ensure compliance with frameworks like the NIST AI RMF and DoD Ethical AI Principles. This further supports the executive orders’ focus on responsible AI development and deployment.
- Encouraging Fairness and Reducing Bias: By relying on curated, unbiased datasets, RAG minimizes the risk of biased or discriminatory outputs, aligning with the executive orders’ commitment to fairness and non-discrimination. RAG can provide equitable access to information by retrieving consistent, non-biased content from a central knowledge base.
- Supporting Innovation and Efficiency: RAG improves operational efficiency by providing contextually relevant, real-time insights to federal employees and the public. Examples include RAG-powered chatbots for federal websites, streamlining public services and reducing agency workload.
- Advancing U.S. Leadership in Ethical AI and Building Public Trust: Deploying cutting-edge RAG systems demonstrates a commitment to state-of-the-art, ethical AI technologies, showcasing U.S. leadership in this domain. Ultimately, by providing accurate, well-sourced, and consistent responses, RAG builds public trust in government AI applications, reducing confusion and misinformation.
Integrating AWS Bedrock and Elastic Cloud
AWS Bedrock offers a secure, managed environment for deploying LLMs at scale. Agencies can select from a range of models and benefit from built-in security features, access controls, and usage policies. This reliable foundation streamlines operations, allowing Federal stakeholders to focus on delivering mission value rather than wrestling with model infrastructure and maintenance.
Elastic Cloud for Semantic Indexing and Retrieval
The Elastic Cloud platform indexes and embeds ingested content—federal regulations, policy memos, research summaries—using semantic vector representations (ELSER embeddings). This semantic index enables the system to understand the user’s intent at a conceptual level, pulling up documents that genuinely answer the query rather than just matching keywords. Such precision ensures that grounded, relevant content is always at the LLM’s fingertips.
RAG Architecture in Practice
Data Ingestion and Indexing
A background web crawler regularly updates the agency’s document repository, stored in Elastic Cloud. As new documents or web content emerges, they are automatically embedded and indexed. This continuous refresh ensures the system’s knowledge base remains current, enabling compliance with the latest regulations and guidance.
Workflow for Each Question (The “Conversation Service”)
When a user poses a question through a user interface or chatbot interface, the Conversation Service (powered by Python and LangChain) orchestrates the RAG workflow:
- Retrieval (Hybrid Search) The system searches both keyword and semantic vector indices to find the most relevant, authoritative documents. This hybrid approach caters to a wide range of queries and content types.
- Augmentation with Grounded Content The retrieved documents are merged with the user’s query into a single augmented prompt. This ensures that the LLM has direct access to the policy clauses, compliance guidelines, or technical manuals needed to produce a correct, policy-aligned answer.
- LLM Generation with Bedrock Guardrails The augmented prompt is passed to the LLM via AWS Bedrock. Here, the Bedrock guardrails ensure adherence to established policies and ethical principles. The model’s response is informed, compliant, and free from improvised content, grounding every conclusion in the retrieved documentation.
User Response and Continuous Learning
The user receives a refined, contextually relevant answer, reducing guesswork and speeding up decision-making. Over time, as queries accumulate, the system adapts—improving both retrieval accuracy and the quality of generated responses, all while retaining its grounding in external documents and adherence to guardrails. Conversation history persistence plays a critical role here by allowing the system to contextualize follow-up questions and maintain coherence across interactions, enabling more personalized and effective responses.
Conclusion
Web search is evolving rapidly with the onset of LLMs. The federal government manages thousands of public-facing websites, each with its own search capabilities. At the same time, federal agencies are moving infrastructure to the cloud at a record pace. This creates a unique opportunity to explore conversational search solutions built on cloud service provider managed services like AWS Bedrock and Elastic Cloud.
RAG offers a straightforward way to modernize search by delivering cost savings, reducing maintenance, and enhancing security. It ensures accurate, relevant responses that are grounded in authoritative sources, building trust and supporting informed decision-making. Adopting RAG helps agencies simplify workflows, meet compliance standards, and provide better service to the public while preparing for future challenges.
Now is the time for agencies to embrace conversational search powered by RAG and take a big step forward in how they manage and deliver information. Reach out to learn more about how RIVA is transforming federal website search capabilities with RAG.
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