AI Prompt Optimizer
Paste any prompt — get a rewritten, more reliable version optimized for clarity, specificity, or instruction-following
Vague or confusing → unambiguous
What Makes a Good AI Prompt?
A good prompt has four properties: clarity (no ambiguity about what you want), specificity (concrete constraints on the output), structure (the model can parse where instructions end and content begins), and examples when needed (1-3 input/output pairs that demonstrate the pattern).
Most prompts that fail to get a good response fail on the first two — they are vague (“write something good about X”) or under-constrained (“write an article”). The optimizer's default goal of clarity and specificity targets these two failure modes first.
How Do You Improve a Prompt That LLM Keeps Ignoring?
When the model partially follows your instructions or skips them entirely, the fix is usually structural. Three changes that consistently work:
- Open with role assignment. “You are an expert X.” primes the model to take a specific stance.
- Use a numbered “rules” section. A list of imperatives (“1. Always... 2. Never... 3. If X, then Y”) is followed more reliably than the same instructions buried in prose.
- Use explicit delimiters around input. Wrap the user's content in triple-quotes (""") or XML tags (<input>) so the model knows where the instructions end and the content begins.
Set the optimizer's goal to instruction-following to automatically apply these structural fixes.
When Should You Add Few-Shot Examples to a Prompt?
Few-shot examples (demonstration pairs of input → output) are most valuable when:
- The output format is unusual or hard to describe (a specific JSON schema, a particular tone).
- The task requires consistent classification (every output should look like the previous outputs).
- You're using a smaller or open-source model that follows instructions less reliably.
They're less valuable for: open-ended creative tasks (where examples could constrain creativity) and one-shot questions (where the example overhead is more than the answer length). The optimizer's few-shot goal adds 2-3 examples that match your task pattern.
How Does Hyperleap AI Use Optimized Prompts?
Every Hyperleap AI agent runs on a structured prompt that combines your brand voice, your knowledge base instructions, and the conversation history. When you optimize the prompt for one agent, it improves every conversation that agent has — across website chat, WhatsApp, Instagram, and Messenger. See how Hyperleap agents use prompts in production →
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