> For the complete documentation index, see [llms.txt](https://docs.tactyc.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tactyc.io/portfolio-construction/faqs/backsolving-to-find-an-ideal-portfolio.md). # Backsolving to find an ideal portfolio Can Tactyc solve for ideal construction parameters? Some GPs would like to set a target performance benchmark (such as TVPI or IRR) - and perhaps have Tactyc generate an “ideal” portfolio for them. If this sounds good to be true - it is! The reality is that there could be an infinite number of “solutions” to this multi-variate problem. Check sizes, graduation rates, exit assumptions, fees, expenses, recycling assumptions - they all play a part in arriving at the final portfolio. Even if we iterate over millions of possible solutions there is no way for a system to make a decision on which is the optimal parameter set from millions of solutions. Secondly, we believe the ***process*** of portfolio construction is at times more important than the end result. By taking the time to play with different assumptions, GP’s can develop an intuition of which levers really move the needs and where their model is most sensitive. We believe this is important for not just the theoretical process of fund construction - but also for actual portfolio management once the fund is launched. --- # 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.tactyc.io/portfolio-construction/faqs/backsolving-to-find-an-ideal-portfolio.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.