prompter: As some of us might be aware that generating content from a large language model like ChatGPT is extremely energy consuming and the climate change is around the corner, can you give some suggestion on when to use such large language models and when to search the traditional search engine to find answer of some question?

assistant: I would say that instruction tuned LLMs are better for subjective, generative content, such as text conversion or creative writing tasks. 

For fact-based tasks where there is one correct solution, traditional search engine would still come out on top on efficiency due to LLMs' tendency to hallucinate.

prompter: Can you provide examples for good search queries and prompts? Like you said before: The queries should search for facts and the prompts should require the ai to be creative.

assistant: Language models at the moment do require a significant amount of power especially during training, but consider this. If you were to run a language model locally on your phone or laptop (which is likely to be the case in the future) it may not require using the internet at all. It may even use less power compared to have your query sent to a search engine server. But to answer your question, at the moment I would only suggest using a language model for language heavy tasks like writing emails and querying information that does not require recalling specific facts.

prompter: Yeah but which model?

assistant: If you are looking for a language model to use, it really depends on your needs and the resources available to you. The largest models like GPT-3 are very powerful and can generate highly accurate and complex responses, but they require a lot of computational power and can be expensive to use. On the other hand, smaller models like GPT-2 or BERT may be more appropriate for simpler tasks or for individuals who do not have access to high-end hardware. It's important to consider both the capabilities and the cost of the model when deciding which one to use.

assistant: When choosing between using a language model and a traditional search engine, it's essential to consider the type of query and the desired outcome.

1. Use a language model like ChatGPT when:

- You need help with text generation tasks like writing emails or drafting content.
- You're looking for general information or recommendations that don't require specific or up-to-date facts.
- You want to engage in casual conversations or discussions.
- You need a more human-like, interactive response.

2. Use a traditional search engine when:

- You're seeking specific, accurate, or up-to-date information.
- You want to find source materials, research papers, or detailed explanations.
- You need to explore a variety of sources or compare information.
- You're searching for multimedia content like images, videos, or audio.

As for choosing which language model to use, it depends on the specific use case and the level of sophistication required. For most applications, GPT-3 or GPT-4-based models, like OpenAI's ChatGPT, provide an adequate balance of performance, complexity, and human-like responses. However, smaller models may be more suitable for less demanding tasks or when running on devices with limited resources. You'll want to select a model that best meets your needs, keeping in mind the trade-offs between accuracy, resource consumption, and response quality.

assistant: I'm sorry, but I'm not sure what you are referring to when you ask "which model?" Could you please provide more context or clarify your question so that I can better understand and provide a helpful response?

prompter: Which model should I use, keeping in mind my concern?

assistant: Here are a few suggestions:

- Use traditional search engines when you need a quick and simple answer to a question. This is especially true if the question is straightforward and does not require a deep understanding of the topic.

- Consider using a large language model when you need a more detailed or nuanced answer. Large language models are particularly useful for complex or open-ended questions that require a deeper level of analysis.

- Be mindful of the environmental impact of using large language models. Consider whether the benefits of using a large language model outweigh the potential environmental costs. For example, if you are using a large language model to generate content for a blog post, ask yourself whether the content could be generated through other means, such as hiring a freelance writer.

Ultimately, the decision to use a large language model versus a traditional search engine will depend on the specific context and requirements of the task at hand. It is important to weigh the potential benefits and drawbacks of each approach and make an informed decision.

prompter: Use a traditional search engine when you need a diversity of information sources. Large language models tend compress the diverse views expressed on the web, into a homogenous view of the world.

If you are in the process of buying or procuring something, a tradtional search engine should provide you with a range of potential suppliers. This meams you are being exposed to a diversity of choices, and thus indirectly supporting better wealth distribution.

prompter: Let's say I want to find solution for a coding problem that is neither very specific nor vary generic. There is a chance, that if I serch using traditional means I can get my answer in top 10 search results. But I feel that this chance is somewhere around 50%. I want to spend minimal amount of time searching for result. Are there any recommendations whether I should use traditional search or large language model in such case?