Hello,
I am from Fanso.io and we have developed a Candy AI clone script specially for entrepreneurs who are interested in building such a platform. We ran into this exact issue during development and want to share what actually fixed it.
The inconsistency most people experience is not a prompting problem. It is architectural.
Characters feel inconsistent because the identity is being reconstructed from scratch on every conversation turn. The system prompt carries all the weight early on, but once the chat grows long and older messages get trimmed to manage tokens, the model quietly loses the thread of who the character is supposed to be.
The fix is separating your character definition layer from your conversation context layer.
Keep a detailed character profile that goes well beyond a system prompt. Document personality traits, speaking style, opinions, emotional reactions, and hard boundaries. The more behavior-specific this profile is, the more consistently the model can follow it. Then on each API call, dynamically inject a compressed character summary alongside the rolling conversation history. Do not pass the full history raw. Periodically summarize older turns into a compressed memory block that preserves personality-relevant moments, emotional reactions, and what the character has revealed about themselves. This keeps context tight without dropping the personality thread.
One thing most teams miss is contradiction handling.
If a character is built to be shy but the model drifts into assertive responses mid-conversation, you need a soft correction layer. This is where PyTorch becomes useful. A lightweight emotion or intent classifier built in PyTorch can run a post-processing check on each model output, flagging responses that contradict the character’s defined behavioral profile before they reach the user. A reminder snippet injected every N turns works as a simpler fallback if you are not ready to build the classifier yet.
Model choice also matters more than people expect.
Smaller or quantized models struggle with persona adherence significantly. For teams who want more control, PyTorch also enables fine-tuning a base model on character-specific dialogue samples, which meaningfully improves consistency compared to relying entirely on prompt engineering. Personality consistency scales with model quality, and something in the Claude or GPT-4 class handles it noticeably better out of the box.
Last point, and this is product thinking as much as engineering: vague personalities break faster. “Friendly and caring” gives the model almost nothing to work with. “Speaks in short sentences, gets flustered when complimented, deflects with humor when uncomfortable” gives it actual behavioral rails. Specificity is what holds the character together over a long session.