The best one-shot prompts to unlock 10x AI

We’re all chasing that “10x” moment with AI – the point where it stops being a novelty and starts becoming a genuine force multiplier for our work.

The truth is it’s often not about complex chains or multi-turn conversations, but about crafting a single, potent one-shot prompt that gives the AI everything it needs to deliver exceptional results.

Most AI interactions produce generic results because most prompts are generic. Well-engineered prompts transform AI from a simple text generator into a strategic partner.
Here are examples that deliver exceptional results.

1. The agile strategist: from document to action plan

The Model: Anthropic Claude Sonnet 4

The Prompt:
<doc>...</doc>
Analyse the document above. It was AI generated so you need to look past the exact text into what is actually meant. I need you to think critically and propose a plan to solve the problem described (not necessarily in the way it is described). Organise the work in a 2-week discovery (to reduce uncertainty and to create an initial prototype), and then 4 one-week sprints to develop the most important features of the MVP. Think in an agile way, deliver value soon, iterate.

Why it’s 10x:

This prompt doesn’t just ask for a summary, it demands:

• Critical analysis: acknowledging the input document might be flawed (AI-generated) and requires deeper interpretation.
• Problem solving: moving beyond the document’s literal description to address the underlying issue.
• Strategic planning: requesting a structured, actionable plan (discovery + sprints).
• Methodology adoption: instructing the AI to “think in an agile way,” focusing on iteration and early value.

Instead of just telling you what the document says, the AI becomes a project planner, outlining how to tackle the problem effectively. This saves hours of strategic thinking and initial planning.

Key characteristics:

• Contextual awareness: “It was AI generated so you need to look past the exact text…”
• Role assignment: “Think critically,” “think in an agile way.”
• Specific framework: “2-week discovery,” “4 one-week sprints,” “MVP.”
• Clear goal: “Propose a plan to solve the problem.”

2. The automated release notes scribe: diff to done

The Model: DeepSeek R1

The Prompt:
Write the release notes based on the git diff below:
<diff>...</diff>

Why it’s 10x:

This is a simple yet incredibly powerful prompt for developers. Manually writing release notes is tedious, error-prone, and time-consuming.

• Automation of a core task: directly translates technical changes into human-readable updates.
• Time savings: frees up developer time for coding rather than documentation.
• Consistency: ensures release notes follow a similar pattern based on the diff information.

This prompt takes a raw, technical input and transforms it into a polished, user-facing output, streamlining a crucial part of the development lifecycle.

Key characteristics:

• Specific input format: Clearly states it’s working from a .
• Specific output format: “Write the release notes.”
• Direct instruction: No ambiguity in what needs to be done.

3. The multi-agent code refactor: optimising with precision

The Model: Anthropic Claude Sonnet 4 (Claude Code)

The Prompt:
This code is getting quite big and complex. Use 3 parallel agents to cleanup and optimise the code while maintaining the same functionality. You need to remove more code than you add.

Why it’s 10x:

Code refactoring and optimisation can be a daunting task. This prompt leverages the AI’s ability to process and restructure code at scale.

• Tackling complexity: addresses large, complex codebases that might be overwhelming for a human to refactor quickly.
• Conceptual parallelism: “Use 3 parallel agents” instructs the AI to consider multiple approaches or facets of optimisation simultaneously (e.g., one agent for readability, one for performance, one for dead code removal). Claude Code can actually spin up separate agents, processing the task in parallel.
• Clear constraint & goal: “Maintain the same functionality” and “remove more code than you add” provide crucial guardrails and a measure of success.

This turns the AI into a sophisticated code maintenance partner, capable of improving code quality and reducing bloat significantly.

Key characteristics:

• Problem statement: “Code is getting quite big and complex.”
• Advanced instruction: “Use 3 parallel agents” (enables hidden Claude Code feature).
• Critical constraints: “Maintain functionality,” “remove more code than you add.”
• Clear objective: “Cleanup and optimise.”

4. The “between the lines” analyst: uncovering true meaning

The Model: DeepSeek R1

The Prompt:
Based on this job description, what will be the day to day tasks of this role?<job-description>...</job-description>
You need to read past the job description. This was probably AI generated so we can't take it word for word. What do they actually mean.

Why it’s 10x:

Job descriptions are often filled with jargon, corporate speak, or even AI-generated fluff. This prompt asks the AI to act as an experienced recruiter or industry insider.

• Deep interpretation: Goes beyond surface-level keyword matching.
• Critical evaluation of source: Acknowledges the potential unreliability of the input (“probably AI generated”).
• Inference of intent: Focuses on “what do they actually mean,” seeking the underlying reality of the role.

Instead of a simple list of duties pulled from the text, the AI provides a more insightful and realistic preview of the job, which is invaluable for candidates or hiring managers.

Key characteristics:

• Meta-cognitive instruction: “Read past the job description,” “can’t take it word for word.”
• Focus on true meaning: “What do they actually mean.”
• Contextual input: The itself.
• Specific question: “What will be the day to day tasks?”

Unlocking your own 10x AI moments

These examples share common threads:

1. Be specific: clearly define the task, the input, and the desired output.
2. Provide context: give the AI the necessary background information (documents, diffs, job descriptions).
3. Assign a role or persona: tell the AI how to think (e.g., “as an agile strategist,” “critically”).
4. Set constraints and goals: guide the AI with clear objectives and limitations.
5. Encourage deeper thinking: ask the AI to analyse, infer, or “read between the lines,” especially when dealing with potentially superficial or AI-generated input.

The difference between mediocre and exceptional AI results comes down to prompt precision. Generic requests produce generic output. Specific, well-structured prompts unlock AI’s true capability as a thinking partner.

Start with these patterns, adapt them to your workflow challenges. The time you invest in crafting better prompts pays back immediately in higher quality results.