DeepSeek Prompt Generator
DeepSeek rewards precision over politeness. Tell PromptVibe your goal and get a numbered, unambiguous prompt with the output format stated twice — free, no account needed.
A DeepSeek prompt generator produces prompts suited to reasoning-capable open-weight models: an explicit task statement, numbered requirements, and a clearly specified output format. Because these models reason at length before answering, prompts that state the format up front and restate it at the end drift much less on long instructions.
How DeepSeek prompts differ
- Reasoning models need a clear finish line: A model that thinks before answering benefits from knowing exactly what "done" looks like. Stating the deliverable and its shape prevents a long reasoning pass from ending in a summary you did not ask for.
- Numbered requirements beat prose: A paragraph containing five requirements is easy to partially satisfy. Five numbered lines are easy to check, both for the model and for you.
- Separate thinking from output: Saying "reason through this first, then give only the final answer in the format below" keeps the working out of your deliverable without suppressing it.
- Politeness padding costs clarity: Hedging phrases like "if possible" and "maybe you could" read as optional. If a requirement is required, state it plainly.
Techniques that work
- Restate the format at the end. Repeating the output specification as the final line of a long prompt measurably reduces drift — it is the last thing the model reads before answering.
- Give the shape, not just the type. "Return JSON" is weak. Showing the exact keys you expect is strong.
- Say what to do when information is missing. "If a required detail is missing, list the assumption you made rather than inventing a fact" stops silent fabrication.
- Cap the length explicitly. Reasoning models are verbose by default. A word or item limit is a real constraint worth stating.
Example DeepSeek prompts
Structured analysis — TAG
Task: Analyse the pricing page copy below and identify what is stopping visitors from converting. Requirements: 1. Quote the exact phrase you are criticising before each point. 2. Give at most 6 findings, ordered by likely impact. 3. For each finding, state the problem in one sentence and the fix in one sentence. 4. Do not suggest anything that requires new data you were not given. 5. If a claim depends on an assumption, label it "Assumption:". Reason through the page carefully first, then output only the final answer. Output format — a Markdown table with the columns: | Quote | Problem | Fix | Impact (H/M/L) |
Code task — RISE
Role: A senior engineer reviewing a change for correctness, not style. Input: The function below and its intended behaviour. Steps: 1. Trace the function with two normal inputs and two edge-case inputs. 2. List every input for which it produces a wrong result or throws. 3. For each, name the line and the exact cause. 4. Provide a corrected version. Execution: If the function is already correct, say so and prove it by explaining why each edge case is handled. Do not invent bugs. Output format — exactly three sections, in this order: ## Trace ## Defects (or "None found", with justification) ## Corrected code
Test it before you trust it
A prompt that reads well is not the same as a prompt that works. The Compare AI Lab runs the same prompt through up to three models side by side so you can see which one actually handles your task, and the A/B tester scores two versions of a prompt against criteria you choose. Free members get a comparison every day.
Limitations
- PromptVibe is an independent tool and has no affiliation with DeepSeek.
- Behaviour differs between DeepSeek model versions and between hosted and self-run deployments.
- Structured prompts improve consistency; always review generated code and analysis before use.
Frequently asked questions
How are prompts for reasoning models different?
Reasoning models spend effort before answering, so the main risk is a long, thoughtful response that is not the deliverable you wanted. Stating the output format at the start and repeating it at the end keeps the reasoning without losing the format.
Should I ask DeepSeek to think step by step?
Reasoning-capable models already do. What helps more is telling them what to do with the reasoning: think first, then return only the final answer in the specified shape.
Why numbered requirements instead of a paragraph?
A numbered list is checkable. Both you and the model can verify that item four was satisfied; nobody can verify the fourth clause of a long sentence.
Is this an official DeepSeek tool?
No. PromptVibe is independent and has no affiliation with DeepSeek.