Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population if the trial sample is not representative of the population of interest. More specifically, generalizations will be hindered if a trial is not similar to the population with respect to characteristics that moderate the treatment effect. Statistical methods have been developed to assess representativeness and improve upon generalizability by combining trials with data from non-experimental studies. Real-world datasets derived from electronic health records are promising resources that can supplement trial data when applying such methods. However, identifying the right real-world data source with the appropriate characteristics captured can be challenging in practice. In this talk, we will articulate a framework for combining trial and real-world data to assess representativeness and ultimately addressing concerns of generalizability. Through this framework, we will provide guidance on defining the target population of interest, identifying a suitable real-world data source describing that population, harmonizing across the data sources, and drawing meaningful comparisons between the trial and target population. This work will provide researchers with methods and tools to contextualize trial findings within the target population of interest through the use of real-world data.