This chapter provides an overview of the tools and methods used in corpus linguistics, with a focus on their applications in both research and educational settings. It first examines the range of ready-built online and offline tools available to researchers, teachers, and learners, comparing these to do-it-yourself (DIY) tools that can be developed using programming languages such as Python or R. Next, the chapter explores the role of corpus tools at various stages of a research study, including corpus compilation, cleaning, tagging, annotation, and analysis. It then provides a detailed discussion of how tools and methods can be used to analyze language at both the ‘bottom-up’ (e.g., word, phrase, sentence) and ‘top-down’ (e.g., paragraph, section, discourse) levels, introducing analytical methods, such as key-word-in-context (KWIC) concordances, concordance plots, clusters, n-grams/lexical bundles, collocates, word frequencies, and keywords. Finally, the chapter explores recent advancements in artificial intelligence (AI), particularly the emergence of large language models (LLMs) and their potential impact on corpus linguistics. These technologies have the potential to enhance traditional corpus tools and methods while opening new avenues for corpus-based research, teaching, and learning.