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Key Takeaways
- Large Language Models are probabilistic text generators that interpolate patterns from training data without generating new knowledge or guaranteeing factual accuracy.
- Industrial applications require deterministic systems with reproducible results, making LLMs fundamentally unsuitable for direct control of physical production processes.
- AI systems must be isolated from control loops affecting physical reality through e.g. circuit breakers following established architectural patterns.
- Classical machine learning models like decision trees offer interpretability and transparency required for regulated industries and EU AI Act compliance.
- Hexagonal architecture effectively isolates AI components with well-defined ports and adapters, enabling model replacements without affecting domain logic.
- Local deployment of open-source LLMs with abstraction layers is preferable to relying on vendor-hosted models that change monthly and introduce vendor lock-in risks.
Core Questions Addressed
- What are the fundamental differences between LLMs, generative AI, and classical machine learning models in industrial contexts?
- Why do LLMs fail in industrial applications and what architectural patterns mitigate probabilistic behavior in deterministic systems?
- How can hexagonal architecture and ports-adapters patterns isolate AI components and maintain system resilience when models change?
- What monitoring strategies detect model drift, confidence illusions, and distribution shifts in production environments?
- How should organizations document AI systems architecturally to comply with the EU AI Act and ensure transparency?
- What role does human-in-the-loop validation play in catching hallucinations and ensuring safe AI-assisted decision-making?
Glossary of Key Terms
- Confidence Illusion: A probabilistic metric claiming high certainty (e.g., 94%) that provides no guarantee of correctness, merely reflecting the model’s statistical belief rather than actual accuracy.
- Model Drift: Silent degradation where training data becomes outdated due to environmental or user behavior changes, causing models to produce corrupt outputs without triggering alerts.
- Simplex Architecture: A resilience pattern combining three components—an AI model, deterministic business logic, and a monitor—that switches between systems based on confidence scores and input distribution changes.
- ONNX Format: Open Neural Network Exchange, a standardized model file format enabling model deployment across different hardware platforms and inference engines without code changes.
- Hexagonal Architecture: A design pattern isolating core domain logic and AI components as separate hexagons communicating through defined ports and technology-agnostic adapters, enabling component replaceability.
- Anti-Corruption Layer: An architectural boundary protecting domain logic from external system peculiarities (like AI probabilism) through translation and filtering mechanisms.
Technologies Mentioned
- Open-source LLMs: Llama, DeepSeek for local model deployment as alternatives to vendor-hosted models like OpenAI and Anthropic.
- ONNX Runtime: Standard inference engine for deploying machine learning models across heterogeneous hardware environments.
- arc42: Documentation framework extended by Nikita for AI systems covering data views, model behavior, runtime deployment, and risk management.
- Computer Vision Models: Systems for object detection, classification, and segmentation tasks in defect detection and quality assurance.
- Rule-Based Systems and Classical Models: Decision trees, random forests for interpretable, deterministic decision-making in regulated financial and industrial applications.
- Circuit Breaker Pattern: Resilience mechanism that stops requests to failing AI systems and redirects to fallback deterministic logic or human operators.