Generative AI development is fundamentally different from traditional software engineering. Its outputs are probabilistic, not deterministic. The same input can produce different results. This variability makes it challenging to guarantee quality and predict failure modes without the right infrastructure. Axiom’s data intelligence platform is ideally suited to address the unique challenges of AI engineering. Building on the foundational and components, Axiom provides an essential toolkit for the next generation of software builders. This section of the documentation introduces the concepts and workflows for building production-ready AI capabilities with confidence. The goal is to help developers move from experimental “vibe coding” to building increasingly sophisticated systems with observable outcomes.

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Axiom AI engineering workflow

Axiom provides a structured, iterative workflow—the Axiom AI engineering method—for developing AI capabilities. The workflow is designed to build statistical confidence in systems that aren’t entirely predictable, and is grounded in systematic evaluation and continuous improvement, from initial prototype to production monitoring. The core stages are:
  • Create: Define a new AI capability, prototype it with various models, and gather reference examples to establish ground truth.
  • Measure: Systematically evaluate the capability’s performance against reference data using custom graders to score for accuracy, quality, and cost.
  • Observe: Cultivate the capability in production by collecting rich telemetry on every LLM call and tool execution. Use online evaluations to monitor for performance degradation and discover edge cases.
  • Iterate: Use insights from production to refine prompts, augment reference datasets, and improve the capability over time.

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What’s next?

  • To understand the key terms used in AI engineering, see the Concepts page.
  • To start building, follow the Quickstart page.