Prompt ambiguity: local LLM vs hosted models
Clarify context in prompts to improve local LLM responses.
Add explicit location and intent to prompts.
Summary
The author presents a simple prompt about walking to a car wash and notes how many variables are missing, leading to ambiguous answers. The post contrasts local LLMs with hosted or SOTA models, claiming local LLMs are still smarter and faster for the user. The author uses the local LLM to improve themselves and wants to stop the frustration caused by unclear prompts. The discussion highlights the importance of context and specificity when interacting with LLMs.
Key observations: ambiguous prompt, missing variables, local LLM vs hosted, local LLM faster, user uses it to improve, wants to stop itching.
The article serves as a reminder to provide explicit context in prompts to get better local LLM responses.
Key changes
- Prompt about walking to a car wash is ambiguous
- Missing variables such as location and intent
- Local LLM is faster than hosted models
- User uses local LLM to improve themselves
- The author wants to stop the frustration