If you are developing a strategy for selecting an enterprise LLM, this ranked guide outlines the essential questions to ask about performance, security, and integration. With a projection that 750 million applications will utilize LLMs by 2025, according to Braintrust, a systematic evaluation process is no longer optional—it is a critical business function. This list is for enterprise architects, AI product managers, and engineering leaders tasked with deploying reliable and secure AI solutions. The questions are ranked by foundational importance, starting with output quality and moving toward operational and strategic considerations.
This list was compiled by analyzing expert frameworks and enterprise best practices, prioritizing questions that address functional performance, security vulnerabilities, and integration complexity.
1. How Accurate, Complete, and Coherent Are the Model's Responses?
This question is foundational for any team building applications where trust and correctness are paramount, particularly in customer-facing roles or data analysis tools. Evaluating an LLM's response quality goes beyond simple right-or-wrong checks. It involves a nuanced assessment of accuracy (is the information factually correct?), completeness (does the answer address all parts of the query?), and reasoning (is the logic sound?). According to an analysis on Towards Data Science, teams must move beyond manual checks and establish robust offline evaluation pipelines using curated datasets to measure these qualitative aspects before a model ever reaches production. This initial focus on output quality ranks higher than other considerations because without it, even the most secure or efficient model becomes a liability. Undetected LLM failures in production are estimated to cost enterprises $1.9 billion annually, as reported by Braintrust, underscoring the financial risk of inadequate quality assessment.
The primary limitation of this approach is its resource intensity. Creating high-quality, domain-specific evaluation datasets requires significant upfront investment in time and expertise. Furthermore, the non-deterministic nature of LLMs means that a response can be different yet still correct, complicating traditional assertion-based testing and requiring more sophisticated "LLM-as-judge" evaluation frameworks where another powerful model grades the output.
