(1) would take the most data (probably, depends on your domain) and probably generalize worst, but would be closest to compute-optimal in the end.
(2) doesn't take much data, you can tweak calibration to your needs, and might only take a few minutes on a beefy GPU.
(3) is the way to go if you need a lot of general knowledge, any amount of reasoning, and don't need good calibration. Likely the least inference efficient of the three options for a given accuracy and calibration.
The hard part of (3) is keeping the answers to the questions independent. If you dump them all together with the state into the prompt, the answers to the second question will depend on the first (and the answer to the first if you it step-by-step). You can do it by tweaking the inference process with the right masking or use of batching, or by fiddling with cache control using a provider's API.
(1) would take the most data (probably, depends on your domain) and probably generalize worst, but would be closest to compute-optimal in the end.
(2) doesn't take much data, you can tweak calibration to your needs, and might only take a few minutes on a beefy GPU.
(3) is the way to go if you need a lot of general knowledge, any amount of reasoning, and don't need good calibration. Likely the least inference efficient of the three options for a given accuracy and calibration.
The hard part of (3) is keeping the answers to the questions independent. If you dump them all together with the state into the prompt, the answers to the second question will depend on the first (and the answer to the first if you it step-by-step). You can do it by tweaking the inference process with the right masking or use of batching, or by fiddling with cache control using a provider's API.