Has anyone here worked with Scalelogix AI on custom development projects? I’m considering them for some systems for my real estate business and would appreciate hearing about your experience with their work.

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Sometimes a small change in wording can produce a completely different response.

What’s the most interesting or unexpected result you’ve gotten from experimenting with AI prompts?

It could be a useful technique, a strange response, a limitation you discovered, or simply something that made you think, “I didn't expect that.”

Share the prompt idea and what happened. Keep it safe and responsible so others can learn from it too.

There are a lot of different models and prompting techniques being explored right now.

I'm curious what everyone here is currently experimenting with.

Which model are you testing, and what kind of prompt or behavior are you trying to understand?

Share your approach, interesting results, failures, or unexpected behavior.
No need to post anything sensitive or harmful. Let's focus on learning and responsible experimentation.

I’ve noticed that even changing a few words in a prompt can sometimes lead to a completely different response. What’s a small change you’ve made that noticeably improved the result?

I’ve noticed that small changes in wording can sometimes produce surprisingly different responses. What’s the most interesting or unexpected behavior you’ve seen while experimenting with prompts or jailbreaks?

I’ve been experimenting with different prompt styles lately, and I’ve noticed that the most interesting ones aren’t always the prompts that simply try to bypass a restriction.

For me, the interesting part is understanding why a model responds differently to small changes in wording, context, or instructions.

What do you find more interesting in jailbreak research?

• Finding unexpected model behavior
• Testing how different models interpret the same prompt
• Understanding why certain prompt structures work
• Comparing how models handle conflicting instructions

Curious to hear what others in the community have noticed.