Local LLMs have gotten a lot of attention lately, especially now that tools like LM Studio make them easy to run. But one thing I keep noticing is that people treat them the same as cloud LLMs, expecting the same results they’d get from ChatGPT. Sometimes this could work, but more often the responses will likely be weaker or disappointing, which can lead to people thinking the model itself is the problem.
Usually, the issue is in how the model is being prompted. Local models don’t have the same layers of assistance that cloud models add behind the scenes - they rely much more on the clarity of what you give them. I had the same problem when first setting up my local model, but once I started approaching it differently and being more deliberate with my prompts, the quality of its responses improved a lot and its behavior became more predictable. So if you just got started with a local LLM, shifting how you think about using it could improve the results you get…
Local LLMs behave differently
They don’t adapt to the way you think
When you run a model through a runner like Ollama or LM Studio, what you’re using is a pre-trained model exactly as it was trained. During a normal chat session the model’s weights are fixed, so it isn’t learning from you or gradually adjusting its behavior the way you’d expect it to. It can still use the conversation context to form its responses, as long as the total token count stays within the context window (the model can only use conversation history that fits in its memory limit). But that’s not the same as adapting long-term.
A lot of cloud AI platforms layer extra systems on top of the base model that help with reasoning, retrieval, tool use, and simulated empathy. Local setups usually skip those pieces unless you configure them yourself, if that’s an option. So because of that, local models tend to be more predictable, but also less forgiving. Many of them are smaller, and smaller models rely on the exact wording of your prompt…
Local models could still infer what you mean to an extent because they’re trained on patterns in language, but without the scale and extra systems behind cloud AI, that inference is usually weaker and depends much more on how you prompt. If a prompt is vague, loosely written, incomplete, or grammatically incorrect, the model closely sticks to the literal input instead of trying to guess your intent and filling in the gaps. This is why people sometimes think a local model is underperforming, when the real difference is how directly it responds to what you actually wrote.
How not to prompt a local LLM
Cloud model habits don’t translate well
Casual wording or relying on the model to “figure it out” can lead to disappointing and frustrating responses. It won’t be the same as having a casual chat with Gemini or sending half-baked ideas to ChatGPT, because as I’ve mentioned, it has limited inference capabilities. Because local LLMs tend to take your inputs literally, unclear prompts are usually reflected in the outputs. Here are some prompt examples that a local LLM will probably not handle as well as a cloud model:
[insert text]. Can you make this better?
[insert text]. What do you think?
Help me write something about local AI models.
Convert these notes into something readable.
It doesn’t really know what you consider to be "better" or "more readable", not unless you specify it in the prompt (or perhaps in a system prompt if your runner’s interface has that option).
What to do instead
Be explicit and structured
If you want a local model to do what you need, the key is in clarity, specificity, and structure. Every prompt should tell the model exactly what you want, the format, any examples that make your expectations clear, and examples of what you don’t want either. Start by breaking tasks down into steps. So instead of “summarize these notes and give me an outline”, write “1. Summarize the notes in 3-5 bullet points. 2. Create a hierarchical outline with sections for characters, plot, and worldbuilding.”
Another great way to make it more structured is by using delimiters like ### and ---. This can help the model distinguish context, instructions, and other inputs. Just make sure you’re consistent with which delimiter you assign to which task. If ### means it’s an actionable instruction, don’t use that for your examples. And if your runner or model automatically enforces Markdown formatting, this may not work.
Speaking of examples, or as it’s called in the prompt engineering space, “few-shot prompting”, it helps to create references for your model. For example:
Review: This restaurant was amazing!
Sentiment: Positive
Description: The color of the atmosphere on a clear day.
Object: Blue sky
Input: The project is delayed.
Tone: Formal.
Output: The project timeline has been extended.
Here's one of my more recent prompts to my gpt-oss-20b model in LM Studio:
Task Instructions:
You are analyzing rough UX research notes. Follow the steps below carefully
1. Summarize the main pain points users mentioned in 3-5 bullet points
2. Identify opportunities for improvement or features the product could add
3. Categorize each suggestion as either “Critical,” “Optional,” or “Nice-to-have”
4. Highlight any ambiguous or incomplete notes that require clarification
---
Format Examples:
Note: Users said the app crashes when uploading photos
Pain Point: App crashes during uploads
Opportunity: Improve upload stability
Priority: Critical
Note: Some users suggested adding a dark mode, but didn’t explain why
Pain Point: N/A
Opportunity: Consider adding dark mode
Priority: Optional
Comment: User reasoning unclear
---
Input Notes:
[my notes pasted here]
A local LLM requires a different approach
Local LLMs can be powerful, but they operate differently from cloud models. They don’t adapt as much during conversation, they take your words more literally, and they need clarity and structure. Everyone talks about prompt engineering, but I feel like most of us don’t really do that with cloud models since they usually get what we mean. Prompt engineering for local models makes a lot more sense because you have to guide it to the result you want.