> For the complete documentation index, see [llms.txt](https://docs.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pentaho.com/pdia-try-pdia/archive-flattened-pages/develop-your-pdi-solution.md). # Develop your PDI solution This workflow helps you to set up and configure the DI development and test environments, then build, test, and tune your Pentaho DI Solution prototype. This process is similar to the Trial Download Evaluation experience, except that you will be completely configuring the Pentaho Server for data integration and working with your own ETL developers. If you need extra help, Pentaho [professional services](http://www.pentaho.com/services) is available. The end result of this is to learn DI implementation best practices and deploy your DI solution to a production server. Most development and testing for DI occurs in Spoon. Before you begin developing your DI solution, we recommend that you attend Pentaho [training classes](http://www.pentaho.com/service/training) to learn how to install and configure the Pentaho Server, as well as how to develop data models. This section is grouped into parts that will guide you during the development of your DI solution. These parts are iterative and you might bounce between them during development. For example as you tune a job, you might find that although you have built a solution that produces the right results, it takes a long time to run. So, you might need to rebuild and test a transformation to improve efficiency, and then retest it. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.pentaho.com/pdia-try-pdia/archive-flattened-pages/develop-your-pdi-solution.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.