When using AI systems, what are some best practices for ensuring the results you receive are accurate, relevant, and aligned with your original goals?

That's a very good question. In my response, I am assuming that the question refers to an LLM-based chatbot.
From my experience, the best results are achieved the more context I provide to the LLM. This means providing as much information as possible that describes both the project itself and the project context.
A second very important step is the quality of the request, also known as the prompt for the LLM. This is similar to human communication, where the quality of the question determines the quality of the answer. Therefore, a good prompt strategy is required, for example:
1. Data and context about the project
2. The goal of the request
3. The task that the LLM should fulfill
4. The format in which the output should be delivered.

In subsequent requests, it is possible to build on the context and results of the previous request. It is important that this process takes place within a chat, as otherwise the context is lost.

When using AI systems is very hard to set the precision or accuracy of the responses. I love bringing in the Agile mindset here pretty much imagine if you are mentoring someone you do a Q&A and based on the reponses of your Mentee you give the feedback so that Mentee can align his/her thoughts in the direction that we hint similarly review the AI responses and using our rationale judgement

1- Give Feedback to the AI system

2- Rework on your promp and be specific on what is expected

3- Keep it short and conscise, guage the responses and slowly we can tune the AI system in a way to get the best output

4- Now the Tech. Solution that comes in for accuracy is havig specific set of APIs that talk to real and accurate data sources or use 2-3 outputs of LLMs and then analyze and bring the best in output.