LinkinLove Official blog

Tag: Model quirks

  • Character Prompting: Part 5 – Implementation

    Practical Implementation

    Building on the foundation articles, this guide focuses on the practical reality of making characters work across different AI models and using what LLMs already know. For more detailed Model explanations, look at this article: Basic For-Model Prompting

    The Reality Check: Your Model Determines Your Limits

    Most character creators build elaborate psychological frameworks and wonder why they work brilliantly on Claude but turn into gibberish on their friend’s local Llama setup. Here’s the uncomfortable truth: the model’s capabilities define what’s actually possible, not your prompting skills.

    The Reality of Character Maintenance

    Characters aren’t “build once, use forever” creations. The high quality works require ongoing maintenance.

    This article covers:

    • Model Capability Assessment
    • Leveraging What LLMs Already Know
    • Systematic Testing
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  • Basic For-Model Prompting

    Model Limitations and Testing – Why Your Perfect Character Breaks on Different LLMs

    Note: This was originally the fifth article in the series “Character Prompting”, but it became very Model focused, so I decided to keep it out. However, it will definitely help you to read the foundation articles.

    Note: We’re continuously working on pre-prompts and system-level training to make models more nuanced and reduce these quirks, but it’s still valuable to understand these patterns when creating characters, as we can’t prevent everything at the system level.

    You’ve built a character with solid foundations, clear behavioral patterns, and simple tracking systems. You test it on Claude, and it works perfectly. Then someone tries your character on GPT-4 and says “it’s broken.” You test it on a local Llama model, and it turns into an incoherent mess. You try it on an older GPT model, and it becomes a helpful customer service bot.

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