Comment by sgc
15 hours ago
I think it's decidedly preliminary to compare models using the same .md file, since they respond quite differently to the same input. I try to narrow to the top 2-3 and then refine inputs for each one. For me it's unfortunately not much better than an intuitive process of trial and error.
Gemini is not at all unusable. It is quite usable for the tasks it excels at - to the point that it is the top pick for many tasks and I spend more money there than elsewhere. On the other hand it responds quite differently from the other major models - so that claude and gpt on one hand are similar and gemini requires a different approach. In my opinion people who think gemini is worthless have not learned how to prompt it correctly. Again, it's intuitive and watching concrete response difference due to small input changes, but if I had to summarize it shows its google books / google scholar roots.
I have started experimenting with qwen more than deepseek, but I have not had good results yet. Given the good press I presume I will learn how to interact with it for better results.
Curious if others have similar experiences in comparing models usefully, or if most don't bother with this, or do something else? I mainly use models for highly focused specialty tasks, so this fine tuning makes the difference between usable and unusable. I don't yet have the luxury of defining my preferred workflow and finding the tool for the task. Everything just breaks almost immediately if I try to shoehorn into my preferred flow.
What are your prompting and general tips for using Gemini effectively?
And what use cases do you think it’s best suited for?
General tip is *iterate*. Look at what it does right and does wrong, and refine. My most complex prompt took me 2 weeks of work to get right, and I just spent a half a day improving that even more. Obviously not worth it unless you are going to be be doing something major. In my case it is for 2 years of work, so clearly worth it.
Somebody else mentioned they had great success at math heavy code. I had to develop a complex piece of software that also integrated into 4 existing systems with a lot of poorly documented constraints. I tried with the major models and Gemini provided the most structured solution that would allow me to work on it, add features etc in the future, and it created an MVP in one shot after working through the planning stage in detail. I have managed to work on that code afterwards quite successfully. It is by far the best model for language tasks like OCR and translation. In my opinion the benchmarks, which put it first for this, are far from showing how far ahead it is because it responds so well to iterating on a prompt. So I think it is good to great for a wide variety of things, but you have to iterate. If what you are doing is simple enough you don't need or want to do that, then use the best model you are already comfortable with. For me that type of work is currently done with GPT 5.5.