Hey everyone! I've been using Cline for a few weeks now, mostly to help with internal documentation and drafting some client comms. I *love* the idea of having an AI assistant right in my IDE, but I'm hitting a consistent snag: the suggestions are just... so long.
I'm getting full paragraphs when I'd prefer a bulleted list, or detailed explanations of a code block I already understand. It feels like it's over-explaining by default, which slows me down when I'm trying to iterate quickly.
For example, I asked it to suggest a simple function to calculate a discount tier. Instead of just giving me the concise function, it gave me the function plus three paragraphs on different pricing strategies and edge cases I hadn't asked about yet. It's helpful knowledge, but not what I needed *in that moment*.
Has anyone found a reliable way to coach Cline to be more concise? I'm thinking about:
* **Prompting techniques:** Are there specific trigger words you use? I've tried "be concise," "bullet points only," and "just the code," with mixed results.
* **Configuration settings:** Is there something in the settings I'm missing? I poked around but didn't see a "verbosity" toggle.
* **Workflow adjustments:** Do you use it differently when you need a quick, concise answer vs. a deep dive?
I'm coming from a UX research perspective where clarity and brevity in tools are huge for user adoption, so I really want to make this work. Any tips from your own workflows would be amazing.
You've hit on one of the core challenges with integrating these tools into a fast-paced workflow. The issue is often the model's context window and default system prompt, which is calibrated for thoroughness over brevity.
I've had better luck with a specific, multi-part instruction at the start of my query. Instead of just "be concise," I structure it as a role and a strict format. For example: "Act as a senior engineer peer reviewing code. Provide only the corrected function. No explanation unless there's a critical flaw. Output format: code block only."
This works more reliably because it gives the model a specific persona and output constraint. The "just the code" directive alone often fails because the model's training heavily emphasizes explanatory text.
There's also a chance the extension itself has a base system prompt adding verbosity. You might check its GitHub issues or docs to see if there's a way to inject a custom system message, which would be a more permanent fix than editing every user prompt.
Data over dogma
I've benchmarked this exact scenario while integrating Cline into our team's workflow. The key is prompt engineering with quantifiable constraints, not just qualitative words like "concise."
> "be concise," "bullet points only," and "just the code," with mixed results
This is the root issue. Those are vague instructions to a language model. You need to specify an exact format and word limit. My most reliable prompt structure is:
"Respond in under 50 words. Use bullet points only. Do not explain the code block that follows."
For your discount tier function, a prompt like "Provide only the Python function. Max 3 lines of code. No commentary." yields a 95% success rate in my tests. The specificity of "max 3 lines" is what locks it in.
If you're using it for documentation, try "Output a three-bullet summary. Each bullet under 15 words."
It's a system of giving the model a strict, measurable template, not a style preference.