There are various approaches when training a large language model that can understand and respond to questions. The to do list of a simple implementation could be:

1. Download a pre-trained language generation model or train your own. The first option is often the preferred choice, as the community has already trained very good language models, like GPT-J. The second option is more expensive, because training a large language model requires a lot of computational power and resources.
2. Fine-tune the language model on a question-answering dataset, like Stack Overflow. This way, the language model will generate adequate responses to your questions, avoiding to simply continue the initial prompt that you gave it.  


A more involved approach may be to use Reinforcement Learning from Human Feedback (RLHF) to improve the generated responses, like in the InstructGPT paper. The steps you should follow to implement this model are roughly the following:

1. As before, download a pre-trained language generation model or train your own.
2. Collect demonstration data by generating initial prompts and providing an adequate response to the prompt. In this step, the quality of the responses is of outmost importance as the language model will be fine-tuned on this dataset. It is also possible to incorporate the collected data with other data sets, like Stack Overflow question-answers or some subreddits.
3. Fine-tune the language model on the data collected in the previous point. If you are not sure on how to do it, you can ask me.
4. Rank the responses generated in point 2, a better reply should be ranked higher than a poor one.
5. Train a reward model on the data collected at the previous point, the reward model is trained with supervised learning by predicting the rank of the response.
6. Fine-tune the language model of point 3 with reinforcement learning. The goal is to maximize the reward given by the reward model trained in point 5.

It must be noted that step 2 and 4 can actually be performed simultaneously, like it has been done in the Open-Assistant project from LAION.

I hope this answers your question, ask me if you need anything else!
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