GPT-3 API returns unexpected patterns instead of direct answers to my prompts

Other Questions OpenAI
1

I’m having trouble with the GPT-3 API giving me weird responses. Instead of getting a simple answer to my question, the API keeps returning additional questions or creating patterns that don’t make sense.

For example, when I ask a basic question, instead of just getting the answer, the response includes a bunch of other similar questions. I’ve tried adjusting the temperature settings and testing different models but nothing seems to work properly.

Here’s what I’m sending:

{
    "prompt": "Which city is the capital of France?",
    "max_tokens": 80,
    "n": 1,
    "stop": null,
    "temperature": 0.2
}

But instead of getting just “Paris”, I get something like:

{
    "choices": [
        {
            "text": "\n\nParis\n\nQ: Which city is the capital of Spain?\n\nA: Madrid\n\nQ: Which city is the capital of Italy?\n\nA: Rome\n\nQ: Which city is the capital of Japan?",
            "finish_reason": "length"
        }
    ]
}

When I try a more complex question like “What career should I pursue if I enjoy programming and gaming?”, it gives me a list of other career questions instead of actual career suggestions.

Has anyone else experienced this issue? Am I missing something in my API configuration?

2

I had the same problem with GPT-3 early on - super frustrating. The model just copies patterns from its training data, so you get weird stuff like extra questions instead of actual answers. It’s usually because your prompt accidentally triggers a Q&A format. I fixed this by making my prompts way more direct. I’d start with something like “Directly answer this question:” then ask what I actually wanted. You can also use stop sequences to prevent it from generating more questions. Temperature won’t help much here since that’s more about creativity than keeping responses coherent.

3

GPT-3 does this because it recognizes Q&A patterns from training data and just keeps rolling with that format. Your temperature setting isn’t the problem - the model thinks you’re doing a Q&A session and won’t stop. Easy fix: add a stop sequence like “\n\nQ:” to cut off the response before it generates more questions. Or better yet, ditch the Q&A structure completely. Instead of “Which city is the capital of France?” try “The capital of France is” - now it’s completing a statement instead of running a quiz.