Best Practices for ChatGPT Long Prompts

Why I Started Caring About Long Prompt Issues

As a content creator who's been working with AI for over two years, I've experienced that crushing disappointment too many times: spending half an hour crafting what I thought was the "perfect" long prompt, only to have ChatGPT return a pile of useless rambling. It's like preparing an elaborate dinner for guests who just poke at their food and leave.

The most memorable incident was last fall when I tried to get ChatGPT to analyze a complex market report. I wrote an 800-word prompt that included background, requirements, format specifications, and detailed instructions. The result? It completely ignored my most important analytical angle and instead went on and on about irrelevant details.

That moment made me realize: long prompts don't equal good prompts. In fact, they're often the enemy of effectiveness.

Why Long Prompts Often Disappoint

After countless trials and errors, I've identified the main reasons why long prompts fail:

First, confused objectives. We get greedy and try to make AI accomplish multiple tasks at once. It's like asking someone to cook dinner, do laundry, and tutor kids simultaneously—you can imagine how well that turns out.

Second, key information gets buried. Imagine trying to find an important phone number in a 500-page book with no index. AI faces the same challenge when dealing with lengthy prompts.

Finally, contradictory instructions. I've seen too many prompts like this: "Please answer concisely but include all details." How is that not setting AI up for failure?

My Core Principles

After years of trial and error, I've developed my own set of principles. These aren't fancy theories—they're lessons learned from painful experience:

One prompt, one goal. This is my iron rule. Every time I'm tempted to add "and also please help me with..." I force myself to stop and write a separate prompt.

Structured thinking. I treat every prompt like a job specification: role definition, specific task, input materials, execution rules, output format. Crystal clear, no ambiguity.

Constraints matter more than freedom. This sounds counterintuitive, but I've found that giving AI clear boundaries actually produces better results. It's like setting rules for children—creativity within a framework is often more valuable.

Use lists, not long paragraphs. AI's ability to process structured information far exceeds our imagination. I now rarely write paragraphs longer than two lines—everything becomes bullet points.

Define output format. This is the most important lesson I've learned. If you don't tell AI what kind of output you want, it will interpret based on its own understanding, which often differs vastly from your expectations.

My Prompt Template (Copy-Ready)

This is the template I've refined through countless iterations, suitable for almost any complex task:

# Role Definition
You are an experienced <domain> expert, prioritizing accuracy and practicality.

# Task Objective
Create <specific deliverable>, focusing on optimizing <quality standards/goals>.

# Input Materials
- Background: <brief context>
- Reference materials: <links/excerpts/data>
- Constraints: <time, budget, scope, style requirements>

# Execution Rules
1) Strictly follow the output format
2) Clearly cite information sources or assumption basis
3) When information is insufficient, ask for the most critical missing information

# Execution Steps
Step 1: Extract core requirements
Step 2: Identify and resolve ambiguities
Step 3: Generate initial draft
Step 4: Self-check against constraints

# Output Format (JSON)
{
  "summary": "string",
  "key_decisions": ["string array"],
  "risk_points": ["string array"],
  "next_actions": ["string array"],
  "final_output": "string"
}

# Quality Check
- Reject outputs that don't meet format or constraints
- Prioritize concise language and specific actions
- Ensure logical coherence and evidence-based conclusions

# Final Response
Return only valid JSON matching the above format

Real Case Study: Complex Analysis Task

Let me share a real example. Last month, I needed to prepare a technical report summary for our board of directors:

# Role Definition
You are a senior technology policy analyst, skilled at transforming complex technical content into business insights.

# Task Objective
Create an executive summary that highlights business implications and strategic decisions from technical documentation.

# Input Materials
- Report excerpts (3 key chapters): <paste core content>
- Audience: Non-technical executive team
- Constraints: 5-minute presentation time, avoid technical jargon, must flag uncertainties

# Execution Rules
1) Avoid speculative conclusions, clearly mark unknown factors
2) Present trade-offs using pros/cons format
3) Prioritize bullet points, keep sentences short

# Execution Steps
1) Extract objectives and key metrics
2) Identify 3 main themes
3) Analyze risks and mitigation strategies

# Output Format (Markdown)
## Executive Summary
## Three Key Themes
## Risks & Mitigation
## Outstanding Questions

# Final Response
Return only content in the above Markdown format

This prompt helped me complete what would have been a 2-hour task in just 20 minutes, and the quality was better than what I could have written myself.

Safe Methods for Building Long Prompts

Through years of practice, I've developed several "safe" methods that dramatically reduce the chance of long prompt failures:

Chunk by Function, Not by Length

Many people think splitting long prompts into segments is enough—that's wrong. My approach is chunking by function: one prompt handles one complete task. When multiple tasks are needed, I use prompt chains.

The key is ensuring each chunk can stand alone and produce meaningful results. I never split in the middle of a logical unit.

Use "Anchor" Section Labels

I now write prompts like technical documentation, with clear labels for each section: Role/Objective/Rules/Process/Format. This not only helps AI understand better but also lets me quickly locate issues when editing.

Once, I found the output format was wrong and only needed to modify the "Output Format" section instead of rewriting the entire prompt. This modular thinking really saves time.

Position Input Information Near Relevant Instructions

This is a lesson learned from countless failures. Don't dump all input information at the beginning—place each piece of information near its most relevant instruction.

For example, if I want AI to analyze user feedback, I put the feedback content right below the analysis rules, not at the prompt's beginning. This way, when AI executes the analysis task, the relevant information is right there.

Provide Sample Output in Advance

This might be the most underestimated technique. I now include a brief sample output in almost every complex prompt. It doesn't need to be complete, but it should clearly demonstrate the expected format and style.

It's like showing AI a "reference answer"—it makes understanding my true intent much easier. I've found this improves output quality by at least 30%.

Add Self-Check Steps

I always end complex prompts with: "Before providing your final answer, check: Does this meet all format requirements? Does it address the core question? Does it follow all constraints?"

Just like we proofread articles for typos, getting AI into this habit significantly improves results.

Pitfalls I've Fallen Into

Let me honestly share some mistakes I've made, hoping you can avoid them:

The Disaster of Mixed Objectives

Last spring, I wrote a prompt asking ChatGPT to both summarize a technical article and invent new features based on it. The result? A hybrid output: shallow summary and poorly-founded new features.

That experience taught me that greed is the biggest enemy of long prompts. Now I'd rather write two simple prompts than one complex one.

The Hidden Constraint Trap

Once, I wrote "assume the project launches next week" buried in a long background paragraph. AI completely ignored this time constraint and gave me a three-month implementation plan.

Since then, I list all constraints separately with prominent formatting. Important information should never be hidden.

Output Format Chaos

What frustrated me most was inconsistent output formats. Sometimes I'd request JSON and get Markdown; sometimes I'd ask for tables and get paragraphs. This inconsistency made post-processing extremely difficult.

Now I always include a concrete example of the expected output format, not just a description.

The Negative Effects of Over-Specification

When I first learned prompt engineering, I went to the other extreme: writing overly detailed prompts. For a simple task, I could write 2000 words, specifying every tiny detail.

The result? AI got confused by all the redundant information and performed worse than with concise prompts. I realized that signal-to-noise ratio matters more than absolute length.

Self-Contradictory Instructions

My most embarrassing moment was requiring both "concise answers" and "comprehensive details" in the same prompt. Faced with contradictory instructions, AI could only balance according to its own understanding, which obviously wasn't what I wanted.

Now I always check for contradictions after writing prompts. Every rule should point toward the same goal.

My Quality Checklist

Through years of practice, I've developed a checklist. Every time I finish writing a long prompt, I use this checklist:

Is the Objective Unique and Clear?

This is the most important checkpoint. I ask myself: If I gave this prompt to a colleague, could they clearly understand what needs to be done?

Specifically, I check:

Is there only one main objective?
Does this objective start with an action verb describing specific actions?
Are the success criteria clear and measurable?

If any answer is "uncertain," I rewrite the objective section.

Are the Rules Clear and Verifiable?

Vague rules are worse than no rules. When I write rules now, I imagine I'm creating a work manual for a new employee.

For each rule, I ask:

Can this rule be judged with "yes/no"?
What are the consequences of violating this rule?
Are there specific examples showing what's right and wrong?

For instance, I won't write "keep it concise" but rather "each bullet point under 20 words, summary under 100 words."

Is the Output Format Single and Specific?

This is the most important lesson I've learned. Chaotic output formats make post-processing a nightmare.

My checking standards:

Is only one output format defined?
Does this format have specific structural specifications?
Is a format example provided?
Are field names clear and unambiguous?

Now I even draw simple diagrams for complex output formats.

Are Examples Consistent with the Format?

This checkpoint is often overlooked but very important. If your examples don't match the required format, AI gets confused.

I ensure:

All examples strictly follow the defined format
Example complexity matches the actual task
Examples demonstrate the expected language style and detail level

Is There a Quality Check Mechanism?

Finally, I always add a self-check step. This isn't just an instruction for AI—it's a reminder for myself.

My quality checks typically include:

Does the output meet format requirements?
Does the content answer core questions?
Does it follow all constraints?
Does the language suit the target audience?

Final Advice

Writing good long prompts isn't something you master overnight—it requires constant practice and adjustment. I suggest you:

Start small. Don't jump into complex prompts immediately. Begin with simple tasks and gradually increase complexity.

Build your own template library. Save effective prompt structures and create your own templates. I now have over a dozen templates for different scenarios, which greatly improves efficiency.

Record failure cases. Every time a prompt doesn't work well, I analyze the reasons and record them. These "negative examples" are often more valuable than success stories.

Exchange with others. Join prompt engineering communities and see how others write. Different approaches and methods will give you lots of inspiration.

If you're working with long prompts that exceed ChatGPT's token limits, try our ChatGPT Prompt Splitter tool. It intelligently breaks down your lengthy prompts into manageable chunks while maintaining context and coherence.

Final Thoughts

Looking back on over two years of prompt engineering experience, my biggest insight is: good long prompts should be like well-designed job specifications, not essays.

They need clear structure, definite objectives, specific constraints, and verifiable output standards. Invest time in polishing these elements, and your AI collaboration experience will improve qualitatively.

Remember, our goal isn't to write the longest prompts, but the most effective ones. Sometimes, deletion is more important than addition.