Writing prompts that get results: a practical guide

Prompting is the future

At 14.ai, we believe prompting will soon be the primary way people interact with software, not through dashboards or buttons, but through clear, structured instructions to intelligent systems. This is already happening in customer support, where fast, repeatable workflows matter most.

Why? Because well-prompted agents don’t just automate tasks, they collaborate with humans to make support teams faster, more consistent, and more effective.

In the same way Google made search literacy essential, prompt literacy is now becoming a core skill. And like writing good code or documentation, writing good prompts takes practice and structure.

AI agents vs. bots: what’s the difference?

Not all automation is created equal. Traditional bots follow rigid scripts and decision trees. They’re fine for simple tasks but fall apart when inputs are unpredictable.

AI agents, by contrast, use large language models to interpret messy human input. They don’t just respond; they take action, such as answering complex questions or processing refunds.

Common types of AI agents in support

AI agents aren’t one-size-fits-all: they come in specialized forms depending on the task. Here are four commonly used agent types:

• Conversational agents chat with customers in real time and help resolve live issues like billing disputes.
• Triaging agents review incoming messages and assess priority, sentiment, and complexity to route them appropriately.
• Drafting agents assist support teams by writing clear, empathetic replies.
• Reporting agents analyze ticket trends and surface insights to inform product, engineering and exec teams.

Each type plays a unique role in making support more scalable and effective.

The building blocks of a support agent

Every AI support agent relies on four key components:

• Instructions: Defines the agent’s role, tone, and scope
• Knowledge access: Provides the docs or data it can reference
• Tools/APIs: Allows the agent to take action, like issuing refunds or tagging tickets
• Workflows: Embeds the agent into structured processes with clear inputs and outputs

Together, these determine how the agent understands user input and what it’s empowered to do. Without these building blocks, even the best prompts won’t lead to reliable performance.

Prompting principles

Well-written prompts make the difference between helpful, accurate agents and agents that miss the mark. These core principles help ensure reliability:

• Start with the instruction: Put the main task up front. Don’t bury the goal in the middle of a paragraph.
• Define role and audience: Make it clear who the agent is and who it’s responding to.
• Use specific language: Avoid vague instructions like “solve the issue.” Tell the agent exactly what to do.
• Specify output formats: Whether it’s 3 bullet points or a one-paragraph summary, structure matters.

Golden rule of prompting

If your teammate wouldn’t know what to do based on your instructions, the AI agent won’t either. Prompting is not just input; it's instruction. You’re teaching the agent how to think. The clearer your guidance, the more reliable the output will be.

Getting started with prompting

If you’re not sure how to phrase a prompt, let ChatGPT help you write it. ChatGPT is surprisingly good at refining prompts. You can start with something rough, often called a “meta-prompt”, and simply ask:

Based on this info, write the best possible version of a system prompt for my agent.

This often returns a cleaner, more structured prompt with clearer tone, constraints, or fallback logic.

Our tips

• Use ChatGPT like a prompt co-pilot.
• Include examples or failed outputs to guide it.
• Be specific about what isn’t working and what you want to improve.

Improving prompts over time

ChatGPT can help refine your prompts based on real-world examples where things go wrong. Just provide a specific case where the output failed, and explain how. For example:

When the message is just ‘Hello’, this prompt outputs a greeting like ‘Hello, how can I assist you today’. That’s not a summary, it’s a response. Rewrite the prompt to only return a short, natural-language thread title that reflects the user’s actual question.

You can include formatting rules, length requirements, and output examples all in a single update prompt.

This helps GPT return a cleaner, more production-ready version, especially useful when building templates or agents at scale. Treat each failed interaction as training data for the next prompt version.

Common prompting pitfalls

Even a powerful model can get tripped up by poorly written prompts. Here are two of the most common issues we see:

1. Overly long prompts

When prompts are too long or packed with multiple directions, the agent tends to focus only on the beginning and end. Important details in the middle may be ignored. Keep prompts short and structured for better results.

2. Mixed signals

Contradictory or unclear instructions often cause hallucinations or unreliable outputs. Make sure the tone, format, and goal are consistent throughout the prompt.

Keeping things clear, short, and logically scoped helps ensure agents behave predictably, especially in support settings where accuracy matters.

Tips and tricks

After working with thousands of prompts across real-world support workflows, here are three lessons that continue to make a difference:

1. Use uppercase selectively

Capitalizing key instructions can improve compliance. For example: DO NOT include greetings or sign-offs.

2. Threats can work when Intentional

When used sparingly and intentionally, language that signals consequences can help drive more consistent outputs, especially in prompts where compliance matters.

3. ChatGPT is good at writing its own prompts

ChatGPT often suggests clearer, more structured versions of its own instructions than a human might write. It can often suggest clearer, more structured instructions than a human might initially write, making it a helpful tool for designing and refining prompts before use.

Prompt testing checklist

This simple checklist can save time troubleshooting later:

• Teammate test — Would a colleague know what output to give?
• Clarity check — Is tone or format clearly specified?
• Fallback plan — Is there a defined behavior when information is missing?

Prompt template gallery

Each template follows the 3-part prompt structure and includes fallbacks.

Triage agent prompt template

• Context: A customer message or ticket
• Goal: Classify the issue and assign a priority level
• Constraint: Use High / Medium / Low priority scale; escalate if unclear

Prompt

Summarization agent prompt template

• Context: A long support conversation
• Goal: Generate a clear summary
• Constraint: Use 3 bullet points

Prompt

Drafting agent prompt template

• Context: A customer question or complaint
• Goal: Draft a reply
• Constraint: Friendly tone, under 100 words

Prompt

Reports agent prompt template

• Context: A batch of recent tickets
• Goal: Identify common issues and volume trends
• Constraint: Include issue counts, flag high-risk items, and suggest product improvements

Prompt

At-home exercise: design a prompt template

Key takeaways