构建企业级生成式AI应用：从起步到实践指南

A Guide to Getting Started fromBooz Allen’s GenAI Team Table of Contents 3Introduction 4The GenAI Stack Architecture 5Infrastructure and Platform Layers6LLM Layer6Data and Pipeline Layer7Agent and Capability Layer7UI/Application Layer 8GenAI Stack Practices8LLMOps: Monitoring, Evaluation and ContinuousImprovement9AI GRC 11Conclusion Introduction Generative artificial intelligence (GenAI)is reshaping how enterprises approachknowledge management, decisionmaking, and user interaction. Whilethis technology has immense potential,deploying GenAI at scale within anenterprise requires more than justmodel access—it demands a strategic,layered approach that aligns withbusiness goals, data infrastructure,and governance standards. choosing and orchestrating LLMsbased on task complexity and cost,and preparing high-quality datapipelines to support real-timeand domain-specific use cases.We also emphasize the importanceof embedding human oversight intoAI workflows, rather than relyingsolely on autonomous agents. Beyond architecture, our reportoutlines essential practices forsuccess: implementing robust LLMoperations (LLMOps) for continuousmonitoring and improvement andestablishing strong governance, risk,and compliance (GRC) frameworks.These practices include biasmitigation, security safeguards,and ethical guardrails to ensureresponsible AI deployment. This report presents a comprehensiveframework for building enterprise-grade GenAI applications, structuredaround a six-layer technology stackarchitecture: Infrastructure, Platform,Large Language Model (LLM), Dataand Data Pipeline, Capability andAgent, and User Interface (UI)/Application. Each layer plays a criticalrole in ensuring scalability, security,and performance. By following this structured approach,organizations can unlock the fullpotential of GenAI—deliveringintelligent, reliable, and ethicallysound applications that drivemeasurable business outcomes. Key considerations include selectingthe right deployment model (on-premises, cloud, or hosted applicationprogramming interfaces [APIs]), The GenAI TechStack Architecture While commercial LLMs deliver impressivecapabilities out of the box, these are notenterprise-ready GenAI applications, at least notin a conventional sense. Rather, a GenAI applicationbuilds upon a complex ecosystem of specializedtools and technologies and orchestratedworkflows and techniques. At the same time, organizations need to ensureGenAI applications are scalable and performantenough to serve the most critical missions whilebeing customizable and configurable enough tosolve real problems and deliver real impact. Thisincludes avoiding technology lock-in by buildingextensible, forward-compatible solutions. Achievingthis agility requires the use of standards-based, openarchitectures that enable plug-and-play adoptionof best-of-breed components. To begin with, it is critical to integrate AI systemswith mission-specific knowledge, rules, andworkflows to deliver contextually appropriateoutputs for federal environments. Implementingguardrails, such as fact-checking mechanisms andcontext-aware validations, helps mitigate risks ofhallucination and other errors, improving reliabilityand accuracy. Advanced security measures furtherenable agencies to prevent misuse and safeguardsensitive data and user privacy from external attacks. As we will explore, a GenAI tech stack provides thearchitecture, capabilities, and operating structureneeded to fill this void. The key components or layersof a GenAI tech stack that integrates engineering bestpractices include: Agents & Capability UI/Application Tools and techniques usedto coordinate and execute anautonomous workflow. End-user software applications,including web interfaces,desktop applications, mobileapps, to command-lineinterfaces (CLI) through whichusers access and use the LLM. Data and Data Pipeline Mission-specific data used forsteering or training models. Governance, Risk, andCompliance (GRC) Large LanguageModel (LLM) Interrelated disciplinesused to operate GenAIsystems securely, safely,and responsibly. Complex algorithms and data tolearn patterns and structuresof language, allowing themto generate human-like text,speech, and images. LLMOps The pipelines necessaryto rapidly design, develop,test, evaluate, deploy,and continuously monitorand improve generativeAI solutions. Platform Software and tools that enablethe development, deployment,and management of LLMapplications. Orchestration Infrastructure Workflow/function-basedor agentic frameworks andcommunication protocols. Underlying physical and virtualresources required to supportthe model (e.g., hardware,storage solutions, networkingresources). Infrastructure andPlatform Layers Infrastructure refers to the physical or cloud-basedresources that power data storage, processing, andAI computations. A robust infrastructure ensuressystems can efficiently manage large datasets andcomplex computations, particularly for real-tim