Mainstream Large Models & Prompt Techniques for Reliable AI Outputs

In today's era where AI is rapidly reshaping productivity, large models are no longer just a research concept—they have truly arrived on every enterprise and individual's desk. The intelligent tools, code generators, copywriting assistants, and even search engines we use daily may all be calling these large models behind the scenes.

Mainstream Large Models Currently Available

First, a quick list so you have a mental map.

1. OpenAI (ChatGPT / GPT-4)

ChatGPT (powered by the GPT series) is one of the most well-known large language models.

It has long led the industry in code, writing, reasoning, and dialogue capabilities.

GPT-4.5 is already in testing, and GPT-5 is speculated to arrive within the year.

2. GitHub Copilot / Copilot X

This is a collaboration between GitHub and OpenAI, fine-tuning the GPT model specifically for programming.

It excels at code completion, unit test generation, and documentation comments.

Ideal for developers to invoke during daily coding.

3. Baidu ERNIE

Baidu's self-developed large model, notable for Chinese understanding and knowledge enhancement.

It has landed applications in writing, marketing, code, and other directions.

4. ByteDance Doubao

Launched by ByteDance, positioned for multi-scenario generation.

Supports text-to-image generation, marketing copy, product reports, video scripts, etc.

Richly integrated within Toutiao and Douyin.

5. DeepSeek

The recently rising DeepSeek has gained popularity with its 'open-source and commercially usable' licensing, performing exceptionally well in Chinese SQL and code generation.

Widely welcomed by domestic developers and data analysts.

Also supports API and private deployment.

Other models are not listed exhaustively.

How to Accurately Describe Problems or Requirements to AI

Many people using AI feel 'why does it never hit the mark?' The key lies in: whether your question is clear and whether the context is sufficient.

Below are several practical 'one-shot' tips:

1. Explicitly State the Expected Output Format

Don't just say: Help me write a Spring Boot demo. Instead, say:

Help me write a Spring Boot demo, requiring JPA to connect to MySQL, including a simple UserController entity, using Chinese comments in the code, and printing Chinese logs.

2. Provide Background & Input Examples

For example:

I have a user table with fields id, name, age, phone. Please help me write the corresponding Mapper, XML, and Service using MyBatis, and generate an insert example.

This hits the mark far better than just saying 'help me write MyBatis'.

3. Tell the AI Where You'll Use It

For instance:

I need to use this in a WeChat Official Account article; please generate text with short sentences, multiple paragraphs, and subheadings.

This lets the AI automatically optimize for a style suitable for WeChat article layout.

4. Start with a Small Request, Then Refine Gradually

For example, first say:

Please write a Python example for reading Excel.

After the AI responds, you then say:

Also add writing to MySQL, and print 'Starting to import user data' before each row.

Conversational refinement like this is easier to get precise than dumping a pile of requirements at once.

Final Summary

AI is already a super assistant, provided you first become a skilled 'requirement analyst' who knows how to ask questions.

Know what you want, converse in a clear, specific, and incrementally supplemented way, and you'll find AI's output directly usable, greatly boosting your productivity.