Practical tactics to build reliable AI apps — Dmitry Kuchin, Multinear

Core Problem & Solution - Traditional software development lifecycle is insufficient for AI applications due to non-deterministic models, requiring a data science approach and continuous experimentation [3] - The key is to reverse engineer metrics from real-world scenarios, focusing on product experience and business outcomes rather than abstract data science metrics [6] - Build evaluations (evals) at the beginning of the process, not at the end, to identify failures and areas for improvement early on [14] - Continuous improvement of evals and solutions is necessary to reach a baseline benchmark for optimization [19] Evaluation Methodology - Evaluations should mimic specific user questions and criteria relevant to the solution's end goal [7] - Use Large Language Models (LLMs) to generate evaluations, considering different user personas and expected answers [9][11] - Focus on the details of each evaluation failure to understand the root cause, whether it's the test definition or the solution's performance [15] - Experimentation involves changing models, logic, prompts, or data, and continuously running evaluations to catch regressions [16][18] Industry Specific Examples - For customer support bots, measure the rate of escalation to human support as a key metric [5] - For text-to-SQL or text-to-graph database applications, create a mock database with known data to validate expected results [22] - For call center conversation classifiers, use simple matching to determine if the correct rubric is applied [23] Key Takeaways - Evaluate AI applications the way users actually use them, avoiding abstract metrics [24] - Frequent evaluations enable rapid progress and reduce regressions [25] - Well-defined evaluations lead to explainable AI, providing insights into how the solution works and its limitations [26]