ryan
ef3a438048
The local model is decode-bound, so output length drives latency. The system prompt asked for "thorough, detailed answers" and to "think things through", which lengthened both answers and reasoning. It now leads with the answer and expands only on request. Per-integration tool lists that duplicated the bound tool docstrings collapse to one paragraph of routing and duplicate-avoidance rules. The prompt drops from about 1,446 to 897 tokens and stays static, so the llama-server prompt cache still hits. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
21 lines
839 B
Python
21 lines
839 B
Python
from blueprints.conversation.prompts import SIMBA_SYSTEM_PROMPT
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def test_prompt_defaults_to_concise_answers():
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assert "concisely by default" in SIMBA_SYSTEM_PROMPT
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assert "thorough, detailed answers" not in SIMBA_SYSTEM_PROMPT
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# Reasoning tokens cost as much decode latency as answer tokens on this
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# hardware, so the prompt must not nudge toward more deliberation.
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assert "think things through" not in SIMBA_SYSTEM_PROMPT
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def test_prompt_keeps_key_behavioral_rules():
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for phrase in [
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"you MUST use the simba_search tool",
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"cite the provided Citation URLs with Markdown links",
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"never invent source IDs or references",
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"search before creating to avoid duplicates",
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"Use save_user_memory when the user explicitly asks",
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]:
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assert phrase in SIMBA_SYSTEM_PROMPT
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