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Building the Foundation: Technical Components for Enterprise GenAI Success
- Enterprises venturing into generative AI must understand the key technical components for success, regardless of technical expertise. This article demystifies GenAI by outlining fundamental building blocks in accessible terms.
- The core components of any enterprise GenAI application include Models (such as large language models), Data Integration (linking AI to business data), Orchestration and Integration (connecting systems), and Safety and Governance (ensuring responsible outputs).
- Selecting the right model involves considering factors like context window size, performance, cost structure, and capabilities, tailored to specific use cases.
- Connecting foundation models to business data is crucial for relevance and usefulness. Retrieval Augmented Generation (RAG) is a common approach that indexes enterprise content, retrieves information, and incorporates it into prompts for models.
- Orchestration involves integrating GenAI with existing workflows using APIs, middleware, and orchestration tools to manage the flow of information between components.
- Safety and Governance ensure AI systems behave appropriately through input guardrails and output filtering, safeguarding against harmful questions or inaccurate responses.
- Organizations are adopting platform approaches for GenAI development, leveraging centralized services for LLM proxy, data and knowledge services, evaluation and feedback systems, among others.
- Strategic technology choices, such as proprietary vs. open source models and build vs. buy for infrastructure, impact technical debt and operational efficiency.
- Building a strong technical foundation empowers enterprises to make informed technology decisions, derive business value from AI, and ensure sustainable success in the evolving GenAI landscape.
- Continuous attention to data security, quality, and governance is essential for maximizing the benefits of GenAI technologies in enterprise settings.
- Next steps for enterprises include structuring teams and governance processes effectively to manage AI initiatives at scale, driving long-term success in leveraging GenAI capabilities.
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