Getting to production-grade AI: Validating LLM Systems

Validating Generative AI Systems in Life Sciences: A Comprehensive Approach

Generative AI has shown immense potential in various industries, and the life sciences sector is no exception. From automating document creation to generating regulatory submissions, AI-driven systems offer an exciting frontier for increasing efficiency and accuracy. However, there is a notably low margin for error in the space. In this high-stakes arena, for teams building LLM-based systems, the bar for validating generative AI systems is significantly elevated.

The Challenge of Infinite Outcomes

One of the primary difficulties in validating generative AI systems is the infinite nature of their potential solutions. Unlike traditional deterministic software, where inputs and outputs can be tested against a fixed set of expected results, generative AI models—especially those based on large language models (LLMs)—are inherently probabilistic. This means that the same prompt can yield different outputs on different occasions, which poses challenges for validation and reliability.

This variability makes it difficult to trust the system, and trust is crucial for unlocking true efficiency gains, as our team has written about in one of our other blog posts. If users cannot consistently rely on AI-generated outputs, the manual review or editing process can end up consuming more time than the system saves, defeating the purpose of automation in the first place.

The Elusive Goal of Building Reliable Generative AI Systems

Achieving reliability in generative AI systems is no simple feat, particularly because the challenges extend beyond the LLM itself. While validating system performance at the proof-of-concept (POC) stage is often straightforward—where manual checks can be applied to limited outputs—scaling this process is a different story. Validating an AI system requires a holistic approach that considers both the human and technical elements, each with its own set of complexities.

The Human Factor: Emphasizing Human-in-the-Loop Validation

Human-in-the-loop (HITL) validation is a crucial component of building and maintaining trust in generative AI systems. Due to the inherent variability of LLMs, full repeatability is difficult, if not impossible, to achieve. This is where human interaction becomes indispensable. Humans can evaluate AI outputs, provide feedback, and make necessary edits, ensuring the final product aligns with real-world requirements.

An often-overlooked aspect of human validation is tracking the interactions users have with AI-generated content. Monitoring how much human editing is required, and precisely what changes are being made, is critical. This not only provides insights into how well the system is performing but also generates validation datasets. Over time, the human-modified content becomes a valuable "ground truth" benchmark, against which future AI systems can be validated.

The Technical Side: Pipeline Evaluation and Modular Validation

While human validation is essential, it is only part of the equation. Focusing solely on output quality is not sufficient for building a fully validated and reliable system. A more technical approach is required to ensure each step of the LLM system pipeline is functioning correctly.

1. Document Ingestion: Ensure documents are being ingested into the system in a structured and consistent manner. Any errors at this stage can cascade into incorrect or incomplete AI outputs.

2. Document Storage: Properly storing documents is crucial for maintaining data integrity. In regulated environments like life sciences, ensuring secure, compliant storage solutions is essential for future validation efforts.

3. Information Retrieval: Retrieving the right information from a database or knowledge base is the bedrock of producing accurate generative outputs. Missteps here could lead to AI systems generating irrelevant or incorrect content, undermining trust.

4. Content Generation: Finally, the actual generation of content must be rigorously monitored. Here, you can track various metrics such as relevance, accuracy, and consistency, adding a granular layer to your validation efforts.

By evaluating each of these components, organizations can gain deeper insights into their AI system’s performance. Tracking key metrics at each stage allows for more granular visibility and helps modularize the validation process. This modular approach is particularly advantageous when upgrading parts of the system, as it minimizes the need for extensive re-validation across the entire platform.

Conclusion: The Path Forward

Validating LLM-based systems will continue to evolve and will require complex, never-fully-satisfactory approaches.

The real question is whether teams are approaching the validation problem as comprehensively as possible.

The mutli-layered approach described here is one that Artos builds into its platform, making it easier for teams to more clearly understand how the systems and work and achieve reliable, production-ready applications faster.

In the end, the goal isn’t just to build AI systems that work—it’s to build AI systems that can be trusted. And trust, in the world of AI, is the key to unlocking its full potential.