AI application development best practices: From prototype to production

The development of AI applications, particularly those utilizing Large Language Models (LLMs), necessitates a shift from traditional programming paradigms. Unlike deterministic programming, LLMs are probabilistic and context-sensitive, producing varied outputs from similar inputs. This requires new development and quality assurance methods, such as creating hypotheses and testing the AI’s responses to ensure consistency. Developers must be familiar with advanced LLM concepts and techniques like Retrieval-Augmented Generation (RAG) and text-to-SQL for handling specific data types. Security, privacy, and bias protection are critical, with measures like data masking and prompt injection prevention. Evaluating AI applications involves creating test sets for consistent monitoring of accuracy, resource usage, and alignment with communication guidelines. Runtime experimentation with LLM configurations and prompts is vital for iterative development. Frameworks and tools like LaunchDarkly AI Configs facilitate this by allowing developers to manage AI configurations without redeploying applications, supporting the iterative nature of AI development.

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