How to Build an AI Engineering Stack

An effective AI engineering stack for building, testing, and deploying LLM-powered applications comprises a variety of elements, including prompts, models, evals, and debugging workflows, and emphasizes the importance of starting with workflow requirements rather than model selection. It suggests defining the application's job and understanding the workflow before choosing a model, as different applications have different requirements, such as latency or auditability. The stack should include layers for product workflow, prompt management, model routing, context and retrieval, evaluation, observability, dataset management, deployment, and cost and latency control. Key practices involve versioning prompts, conducting evaluations, maintaining observability, and managing datasets to ensure quality and reliability. Additionally, it advocates for a structured release process to handle prompt and model changes, emphasizing the importance of measuring cost and latency at each step and being cautious with agent use. Ultimately, the right stack facilitates better control over AI applications, enabling teams to ship, test, debug, and improve systems efficiently.

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