Production AI is more than just a fancy LLM

By Rob Boerman | Published on 2024-06-18

Production AI is more than just a fancy LLM

As a tech enthusiast, I've always been fascinated by the process of reverse-engineering successful products to understand what makes them tick. Take the iPhone, for example. When it first launched, it revolutionized the smartphone industry. Many competitors tried to replicate its success by mimicking its sleek design and user interface. However, they often failed to recognize the complex ecosystem of hardware, software, and services that powered the iPhone's seamless user experience.

In the world of AI, I see a similar pattern emerging. Companies are eager to jump on the bandwagon, creating proofs of concept (POCs) that showcase the potential of AI in their domain. However, the path from POC to production-ready AI is littered with hidden challenges.

At first glance, it might seem like all you need is a powerful language model (LLM) to build a successful AI system. After all, LLMs have demonstrated remarkable capabilities in natural language processing and generation. But as any experienced tech professional knows, there's more to the story.

Critical Components of Robust AI Systems

To create a truly robust and effective AI system, we need to consider several critical components:

  1. Specific Context: AI systems must be trained on domain-specific data to provide accurate and relevant insights. Off-the-shelf LLMs often lack the context necessary to solve industry-specific problems.
  2. Planning and Orchestration: AI systems need to be able to break down complex tasks into smaller, manageable steps. This requires sophisticated planning and orchestration capabilities that go beyond simple query-response interactions.
  3. Observability and Explainability: As AI systems become more complex, it's crucial to have visibility into their decision-making processes. Observability and explainability features allow developers to debug, optimize, and trust the outputs of their AI models.
  4. Robustness and Security: AI systems must be resilient to errors, edge cases, and adversarial attacks. Robust error handling, fallback mechanisms, and security measures are essential for production-ready AI.
  5. Data Infrastructure: AI systems require a solid foundation of data infrastructure to function effectively. This includes data storage, processing, versioning, and governance capabilities that ensure the integrity and reliability of the data feeding the AI pipeline.
  6. Deterministic Control Flow: As AI systems become more autonomous and agentic, it's crucial to maintain deterministic control over their actions and outputs. While the idea of free-flowing, self-directed AI agents is intriguing, it introduces uncertainties and risks that may be unacceptable in production environments. To ensure predictable and reliable results, AI systems need to be designed with clear control flow mechanisms that allow human operators to guide, monitor, and override AI decisions when necessary. This balance between autonomy and control is essential for building trust and confidence in AI-powered solutions.

Bridging the Gap: From POC to Production

Building an AI solution that addresses these critical components is no easy feat. It requires deep expertise in data management, process orchestration, system integration, and AI architectures. Companies should carefully evaluate their infrastructure and identify gaps that need to be filled to bring their AI initiatives from POC to production.

Investing in a robust AI platform that abstracts away complexities can significantly accelerate the journey from POC to production. By leveraging a pre-built framework designed with AI in mind, companies can focus on building high-value applications rather than reinventing the wheel. As the AI landscape evolves, success requires a holistic approach that considers the entire ecosystem, from data ingestion to model deployment and monitoring. Companies that invest early in building a strong foundation will be well-positioned to turn their AI visions into reality.

Conclusion

Just as the iPhone's success lies in its seamless integration of hardware, software, and services, the key to production-ready AI lies in the careful orchestration of context, planning, observability, robustness, security, and data infrastructure. When you get all those things right, the AI puzzle becomes manageable and the gap between POC and production becomes navigable. When you do not, you end up with a fantastic set of demo cases that will never end up solving real world problems.