Until just a few years ago, machine translation (MT) was still a niche product. Nevertheless, companies are increasingly incorporating MT into the translation process in order to produce translations in a faster and more cost-effective manner. However, since machine translation still results in errors, post-editing continues to be essential in most scenarios. In this article, we put our focus on the various types of post-editing and their differences. We explain how to successfully integrate them into the translation process. It is only possible to get the full added value of machine translation if you combine it with post-editing, which involves revising the results to improve quality. Post-editing involves modifying machine-generated translations with the objective of improving them. Post-editing is necessary to correct the errors in the machine translation while simultaneously reaping the benefits of the price advantages of machine translation. To put it in simple terms, post-editing is a mixture of proofreading and translating, so the term describes the entire process involved in working with machine-produced translations. The errors that occur in machine translations vary depending on the system. Older rule-based systems generated very choppy translations with poor word order and inappropriate arrangement of clauses. The somewhat more modern statistical systems frequently produced incomplete translations with sentences that were grammatically incorrect. Last but certainly not least, the neural machine translation seen today requires that more attention be paid to content. These systems produce texts of a more stylistically sound and articulate character and have almost no spelling or grammatical errors, making it easier to overlook content errors, especially when they relate to terminology. Regardless of the system used, it is always advisable to carry out post-editing, with the exception of a few narrow use cases. Depending on the requirements that the target text needs to meet, post-editing may not be absolutely necessary if translations are being produced for test purposes or to be distributed as simple brief posts on social media, for example. A distinction is generally made between light post-editing and full post-editing. Regardless of the system used, it is always advisable to carry out post-editing, with the exception of a few narrow use cases. Keep in mind that the quality of the raw translation can influence the decision regarding what kind of post-editing is ultimately needed. If the raw translation is already quite good, it is sometimes sufficient to carry out light post-editing in order to arrive at a high-quality translation. If high-quality translations are absolutely required in your area, but you still want to use machine translation, a customized engine can be the right solution. As suggested by the name, these are machine translation systems that have been trained using the customer’s own data, making it possible to deliver better raw translations. You can find more information on this in the article “Machine Translation for Companies". If the raw translation is already quite good, it is sometimes sufficient to carry out light post-editing in order to arrive at a high-quality translation. Post-editing only becomes truly efficient when it is integrated into the systems so that the terminology database, translation memory, and quality management are directly linked. Machine translation and post-editing have the biggest payoff when used in combination with a translation management system.