---
title: "In-depth Review: PromptPilot, ByteDance's 'Prompt Factory'"
type: "Topics"
locale: "zh-HK"
url: "https://longbridge.com/zh-HK/topics/32418534.md"
description: "$Alphabet(GOOGL.US) $Meta Platforms(META.US)Does this scenario seem somewhat familiar? When you eagerly give instructions to a large AI model, such as &#34;Help me analyze this week's stock price trend.&#34; After waiting for dozens of seconds, you receive a hollow, generic template with only data listings, which is very disappointing. Then it occurs to you, this shouldn't be the case..."
datetime: "2025-07-31T00:49:51.000Z"
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author: "[锦缎研究院](https://longbridge.com/zh-HK/profiles/2576456.md)"
---

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# In-depth Review: PromptPilot, ByteDance's 'Prompt Factory'

$Alphabet(GOOGL.US) $Meta Platforms(META.US)

Does the following scenario feel somewhat familiar?

When you eagerly give instructions to a large AI model, such as "Help me analyze this week's stock price trends."

After waiting for dozens of seconds, you receive a hollow, generic template filled only with data listings, which is extremely disappointing.

Then it occurs to you—this shouldn’t be the case. In the short videos you’ve seen before, AI was portrayed as a magical tool:

Some people use AI to chase fashion trends and generate viral copy for WeChat Moments, Xiaohongshu, and Weibo;

Some use AI to generate high-quality code that surpasses what senior programmers can write;

Some employ AI as industry experts in various fields, effortlessly producing analysis reports;

Yet, the same AI yields vastly different results.

It’s a fact that there are capability gaps between different large AI models.

**But the more significant reason for this disparity lies in the method of using AI—how you ask the questions.**

We’ve noticed that, to lower the barrier to entry and accelerate the adoption of large AI models, major tech companies are investing heavily in "prompt engineering." PromptPilot is ByteDance’s solution platform for large model prompts.

Using it as a case study, this article explores "prompt engineering" and ByteDance’s "Prompt Factory."

## **01 The Evolution of Prompts**

The medium for human-AI communication is the prompt.

Some might ask, "Isn’t writing a prompt just typing a question?"

Not quite—it’s a discipline.

In the short few years since AI’s inception, this discipline has evolved rapidly from the "ancient era" to the "modern age."

A brief look at the development path of prompts might help explain why we need a more "engineering" mindset.

**Stage.1 The "Magic Spell" Era**

This is the earliest stage, but it’s also where most people are today.

Using a large AI model is no different from using a search engine. When people first used GPT-3.5, they could simply throw out questions and treat AI as an encyclopedia.

The characteristics of prompts in this stage are also clear: "one question, one answer, simple and direct."

Of course, the results are somewhat luck-dependent. For tasks requiring thought, reasoning, or creativity, AI’s responses can range from impressive to mediocre.

**Stage.2 The "Guidance and Enlightenment" Era**

Since then, development has far outpaced expectations.

Researchers and "power users" discovered that AI is like a child—sometimes it just doesn’t understand direct commands.

But with proper prompts and guidance, it can "grow" and deliver better results.

This stage gave birth to two milestone approaches to using AI:

1\. Learning by Example: Before formally querying AI, provide examples for it to mimic;

2\. Chain of Thought: Instead of generating a final answer directly, AI is guided to "show its work," like solving a middle school math problem. This approach led to significant leaps in computational, reasoning, and logic-based tasks.

Thus, while AI is fundamentally just pre-written code, it can also be seen as a student that can be taught and inspired.

**Stage.3 The "Systematic Engineering" Era**

This is the era we’re in now. AI products are diverse, and their capabilities have reached "godlike" levels.

Simple tricks can no longer effectively harness AI. It’s time to develop systematic, standardized, and reusable methods to leverage AI more efficiently.

As a result, whether it’s LLM application development platforms or the latest versions of large AI models from various vendors, automatically generated prompts are no longer fragmented commands. Instead, they adopt a structured framework that includes elements like role, context, task, rules, output format, and constraints.

The goal is simple: to make AI’s output stable, controllable, and reproducible.

## **02 Prompt Engineering**

What is "prompt engineering"?

AI’s answer: "A science of designing and optimizing prompts to communicate more effectively with large language models, guiding them to produce more accurate, relevant, and higher-quality outputs."

As mentioned earlier, the importance of prompts stems from the "Garbage In, Garbage Out" principle—the quality of an AI model’s output depends directly on the quality of the input prompt.

**The most critical function of a high-quality prompt is to effectively reduce the likelihood of AI "hallucinations," making its responses more aligned with reality and user intent.**

At the same time, prompts help unlock AI’s "potential." Compared to simple commands, prompt engineering enables AI to perform more complex and abstract tasks, such as coding, market analysis, and creative generation.

Additionally, users can impose constraints on AI’s responses—such as format, tone, and length—to flexibly adjust how answers are presented while saving debugging time.

For writing prompts, Gemini 2.5 Pro proposes the $Ryder(R.US).L.E.S. framework. Here’s an example used in workflow construction:

R - Role: Who do you want AI to be?

This is the step most easily overlooked by AI users, as we often jump straight to asking questions. By assigning AI a specific and professional role, we activate its knowledge base in that domain, making its responses more in-depth and professional.

**Example:**

\# Role

You are a top-tier financial data analyst capable of parsing user requests and preparing a precise list of data requirements for subsequent database retrieval programs.

O - Objective: What core task do you want to accomplish?

Users need to clearly tell AI the ultimate goal. Don’t worry about being overly wordy—AI struggles with short phrases, not long sentences. The clearer the task, the more precise AI’s path of action.

**Example:**

\# Core Task

Your task is to analyze the user’s instruction "{{query}}" about "Google" (also known as Google). Your final output is not a direct answer to the user’s question but a list of \*\*all basic data fields\*\* that must be retrieved from our financial knowledge base to answer it.

L - Limit & Constraint: What rules must be followed?

AI, as a free-spirited writer, tends to produce output in an unrestrained manner. To get accurate results, we must impose constraints, including but not limited to style, tone, word count, and prohibitions.

Example:

\# Output Requirements

\- \*\*Strict Format\*\*: Output must strictly consist of \`key:value\` pairs, commas \`,\`, and semicolons \`;\`.

\- \*\*Clean Output\*\*: Do not include any prefixes, explanations, quotes, spaces, or any other extraneous text.

\- \*\*Empty Handling\*\*: If the instruction is entirely unrelated to Google’s financial or market data, output \`NO\_QUERY\`.

E - Examples: Are there any reference examples?

If the user’s request is complex or niche (e.g., in a specialized field), providing AI with a concrete example is an efficient way to communicate. This helps AI quickly grasp the expected format and style without multiple rounds of trial and error.

**Example:**

\# Example

\* \* User says \*: "What are Google’s revenue and market cap?"

\* \* Your thought \*: The user directly asks for "operating revenue" and "market cap." These are basic metrics.

\* \* Final output \*: operating revenue, market cap

S - Steps: How many steps are needed to complete the task?

For more complex tasks, the previously mentioned "chain of thought" comes into play. Dumping the entire task on AI at once usually doesn’t yield ideal results. But if we guide AI to think and execute step by step, we can significantly improve the logic and accuracy of the output.

**Example:**

\# Steps

First, determine whether the financial metrics specified by the user can be directly obtained from the given financial statements. If not, retrieve related metrics based on calculation formulas. Finally, analyze the meaning of these metrics.

A few details to note about this framework:

First, there are no strict rules for writing prompts—the five components above can be adjusted as needed.

Second, examples may contain symbols like {}, \*\*, or <\>. These are small tricks in prompt engineering, where each symbol has a specific function, turning a natural language paragraph into a "program-like" set of instructions.

## **03 ByteDance’s "Prompt Factory"**

After clarifying the prompt-writing process, it’s time to consider the practicality of this method.

Clearly, following each step meticulously makes the workload akin to writing an essay.

From another perspective, we need prompts to get AI to complete tasks—and writing prompts is itself a task.

So, let’s take another shortcut: have AI write the prompts for us.

In June, ByteDance launched a product called PromptPilot, a full-chain optimization platform for large model applications.

The platform’s introduction states that it not only provides precise, professional, and continuously iterable prompts but also covers the entire process of large model development—from conception and deployment to iterative optimization.

The product is free to use until September 11, 2025. Link: https://promptpilot.volcengine.com/home

Seeing is believing—let’s try it out to see how it performs.

The main UI looks clean, with three primary functions: Prompt Generation, Prompt Optimization, and Visual Understanding Solution.

Since this article focuses on prompts, we’ll only cover the first two.

Figure: PromptPilot Workflow

For users with no prompt-writing experience, they can directly generate structured prompts on the platform:

Figure: Prompt Generation Interface

The task description doesn’t need to be overly complex—just clear enough. There’s no need to worry about insufficient detail, as adjustments can be made later.

This is the first draft of the prompt generated by the platform. While not yet complete and possibly still differing from the user’s detailed needs, its effectiveness is already far better than using short commands with AI.

Next, we need to verify how well this prompt works. Click "Validate Prompt" to enter the optimization phase:

Figure: Optimization Mode Selection

PromptPilot offers two optimization modes: Scoring Mode and GSB Comparison Mode. Scoring Mode is like "short-answer questions," requiring users to fill in scores, comments (optional), and ideal answers (optional). GSB Comparison Mode is more like "multiple-choice," where users compare answers and judge them as Good, Same, or Bad.

Given the importance of prompts, Scoring Mode is recommended.

Figure: Optimization Interface

After selecting Scoring Mode, several settings are required:

First, if the prompt doesn’t meet the user’s needs, they can use "Rewrite Prompt."

Then, in the prompt, you’ll see familiar placeholders like {{AI\_NEWS}}, indicating where AI-related news should be filled in. Click "Fill Variables" and paste the news content.

PromptPilot also offers a thoughtful feature: AI-generated variable content.

In other words, if we don’t have enough news, we can have AI generate some. This is useful when information authenticity isn’t critical—it’s quickly construct datasets.

However, since we need real news for article publishing, and some smart models can detect fake news, we won’t use AI-generated variables here.

Finally, in the model response window, users can freely select the large model version, with some versions of DeepSeek and Doubao being free.

Here, let’s try Doubao’s new model: Doubao-Seed-1.6-Thinking, with deep thinking enabled.

After completing these settings, click "Save and Generate Model Response." The platform will call the selected model to write the article based on the given prompt. After generation, click "Add to Evaluation Set."

One article alone isn’t enough to judge the quality of a prompt—we need more data for evaluation.

Figure: Evaluation Dataset Interface

As before, we need to paste news into the AI\_NEWS column in the evaluation dataset.

Click "Generate All Responses," and the model begins writing corresponding articles.

Scoring can also be done by AI. Click "Smart Scoring" → "AI Scoring Criteria" → "Generate Scoring Criteria" to obtain a detailed scoring standard, which users can modify or use directly.

With Smart Scoring enabled, AI automatically generates scores and reasoning after writing articles. Note that Smart Scoring isn’t perfect—it often gives full marks, which is meaningless for prompt optimization.

Of course, this step can also be done manually. Subjective reviews can align the article’s style closer to the ideal. But with many news items, manual review becomes time-consuming.

This is where our "old friend" workflow comes in.

Since PromptPilot is a Volcano Engine tool and we’ve chosen Doubao as the model, let’s use ByteDance’s Coze for workflow development.

Figure: AI-Generated Article Scoring Workflow

The workflow structure is simple: the start node takes two parameters—news (original news) and article (AI-generated article).

The large model node handles scoring, taking the same two parameters as input and beginning to write the prompt.

Since we’re studying AI, of course, the prompt should also be generated by AI. Note that increasing the scoring granularity helps optimize prompts.

Thus, I had AI generate a scoring standard, instructing it to judge strictly to create differentiation, and produced the corresponding structured prompt.

Figure: AI Scoring Prompt |

After writing the prompt, the large model node also selects Doubao·1.6·Deep Thinking·Multimodal.

Now, just run the workflow, paste the original news and AI-generated article, and you’ll get a score and reasoning, which can then be pasted into PromptPilot.

Finally, the dataset contains 36 news items and corresponding articles, ready for prompt optimization.

Figure: Smart Optimization Interface

The platform recommends over 50 data points with ideal answers, but this isn’t mandatory. Only scores are required.

Smart optimization took about 17 minutes, iterating 28 times, with the following results:

Figure: Smart Optimization Results

Now, we have an optimized prompt, with more task descriptions and details compared to the previous version.

Scrolling down the page reveals articles generated with the new prompt, which we won’t showcase here.

But remember—prompt engineering doesn’t end here. Further optimization is possible by adding data, scores, and ideal answers until the prompt fully meets requirements.

At this point, we’ve mastered a standardized method for optimizing prompts.

In fact, this process is what we often call—reinforcement learning.

## **04 Conclusion**

Returning to the initial question: Why does the same AI produce vastly different results in different hands?

**The answer lies in prompt engineering—the art and science of efficient communication with AI.**

It’s not exclusive to the computer industry but a fundamental skill for the future.

Mastering it means harnessing AI and amplifying one’s own value.

However, knowing is easier than doing. There’s a gap between "knowing" the importance of structured prompts and "applying" them skillfully in every AI query.

**To be honest, ByteDance’s current version of PromptPilot is far from perfect and not the endgame.**

Don’t expect it to generate a "god-tier" prompt that meets all refined needs with flawless results.

When facing complex or innovative tasks, its framework still feels limiting.

Also, it has a learning curve, requiring time to learn, configure, and adapt.

**But this also reveals PromptPilot’s core value: it’s not an "answer machine" but a "mindset corrector."**

In learning and using PromptPilot, it forcibly breaks our habit of casually asking questions in plain language.

Its structured editor is more like a "mental scaffold"—perhaps not aesthetically pleasing but ensuring the constructed building has a solid foundation and complete structure.

Its existence helps users who struggle with AI chat boxes or are frustrated by poor AI output quality to make the leap from 0 to 1.

Its target users aren’t prompt engineers who effortlessly write hundreds of words of complex instructions but every "student" who wants to move beyond inefficient questioning and start building systematic and structured thinking.

Ultimately, after mastering this mindset, we may no longer need PromptPilot—but we’ll have the foundational ability to communicate efficiently with AI.

And that is the true passport to the AI era.

### 相關股票

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- [Meta Platforms (META.US)](https://longbridge.com/zh-HK/quote/META.US.md)
- [Alphabet - C (GOOG.US)](https://longbridge.com/zh-HK/quote/GOOG.US.md)

## 評論 (1)

- **欧气满满的交易员 · 2025-08-01T02:32:08.000Z**: 🫡
