Optical refractive index (RI) sensors based on resonance techniques are gaining attention due to their potential for simple, low-cost, high-throughput detection. While many RI sensors operate on similar sensing mechanisms, their construction varies. This study defines a rigorous detection limit for resonant RI sensors, accounting for all factors affecting performance. This definition enables a standardized approach for quantifying and comparing the performance of optical resonance-based RI sensors, and guides design strategies for performance improvement. The detection limit (DL) is defined as the ratio of sensor resolution (R) to sensitivity (S): DL = R/S. For refractometric sensing, DL indicates the smallest RI change that can be accurately measured. For biomolecule sensing, it indicates the minimum amount of analyte that can be quantified. The DL is influenced by factors such as RI sensitivity, system resolution, and noise sources. The sensitivity of an RI sensor is determined by the fraction of the optical mode interacting with the sample. The DL is also affected by noise, including amplitude noise and spectral noise. The study uses Monte Carlo simulations to analyze the statistical variance of the mode amplitude maximum value under random noise. The results show that a higher Q-factor reduces spectral noise, improving DL. The analysis also considers the role of the optical mode fraction interacting with the sample. While higher interaction increases sensitivity, it may also increase absorption, reducing the Q-factor and thus the DL. The study shows that for samples with low absorption, increasing the interaction improves DL, while for high absorption samples, increasing interaction has no benefit. The study also addresses biomolecule detection, showing that the DL can be calculated based on the bulk RI sensitivity and the sensitivity to biomolecule capture. The DL is defined in terms of analyte molecules per area or mass per area on the sensor surface. The study concludes that a rigorous method for performance quantification is essential for RI sensors. This method considers both sensitivity and resolution, and accounts for noise sources. For high-Q sensors, temperature control is crucial, while for low-Q sensors, reducing amplitude noise and improving spectral resolution are important. The study provides insights into optimizing sensor design to improve detection limit, with potential applications in various sensing technologies.Optical refractive index (RI) sensors based on resonance techniques are gaining attention due to their potential for simple, low-cost, high-throughput detection. While many RI sensors operate on similar sensing mechanisms, their construction varies. This study defines a rigorous detection limit for resonant RI sensors, accounting for all factors affecting performance. This definition enables a standardized approach for quantifying and comparing the performance of optical resonance-based RI sensors, and guides design strategies for performance improvement. The detection limit (DL) is defined as the ratio of sensor resolution (R) to sensitivity (S): DL = R/S. For refractometric sensing, DL indicates the smallest RI change that can be accurately measured. For biomolecule sensing, it indicates the minimum amount of analyte that can be quantified. The DL is influenced by factors such as RI sensitivity, system resolution, and noise sources. The sensitivity of an RI sensor is determined by the fraction of the optical mode interacting with the sample. The DL is also affected by noise, including amplitude noise and spectral noise. The study uses Monte Carlo simulations to analyze the statistical variance of the mode amplitude maximum value under random noise. The results show that a higher Q-factor reduces spectral noise, improving DL. The analysis also considers the role of the optical mode fraction interacting with the sample. While higher interaction increases sensitivity, it may also increase absorption, reducing the Q-factor and thus the DL. The study shows that for samples with low absorption, increasing the interaction improves DL, while for high absorption samples, increasing interaction has no benefit. The study also addresses biomolecule detection, showing that the DL can be calculated based on the bulk RI sensitivity and the sensitivity to biomolecule capture. The DL is defined in terms of analyte molecules per area or mass per area on the sensor surface. The study concludes that a rigorous method for performance quantification is essential for RI sensors. This method considers both sensitivity and resolution, and accounts for noise sources. For high-Q sensors, temperature control is crucial, while for low-Q sensors, reducing amplitude noise and improving spectral resolution are important. The study provides insights into optimizing sensor design to improve detection limit, with potential applications in various sensing technologies.