Prompt Programming: Transforming AI Interaction from Craft to Systematic Discipline
- To make good use of the capabilities of existing large models, high-quality prompts are crucial. However, writing high-quality prompts is still a craft at the present stage, relying heavily on personal experience.
- Although there are many self-share prompt methods and frameworks from individuals, as well as prompt tutorials by Professor Andrew Ng, the existing prompt creation methods still have various shortcomings:
- This is one of the reasons why people love to collect and share time-tested prompts.
- With the emergence of the GPT-4 model, the dependence on prompts has decreased. Meanwhile, its more powerful basic capabilities provide a good foundation for writing more powerful prompts, making the capabilities of high-quality prompts increasingly powerful and complex.
- Prompt writing is becoming more and more like a programming language in the AI era.
- Then, is it possible to master some basic rules and concepts, as well as some programming patterns (similar to object-oriented programming), to efficiently write well-performing and stable prompts—namely “prompt programming”?
- If “prompt programming” is feasible, it may systematize fragmented skills and reduce dependence on experience through structured thinking. For example, similar to function encapsulation in programming, common prompt modules can be abstracted into reusable “prompt components,” such as “problem decomposers” and “answer validators.”
- This model requires defining standardized “prompt syntax”: such as using specific symbols to mark input variables (similar to {input_text}), using comments to explain logical branches (such as “when the answer contains controversies, trigger the risk assessment module”), and even designing “prompt call protocols” similar to programming interfaces.
- The context understanding ability of GPT-4 makes this structured design possible. For example, by presetting a “system prompt” to fix the role (such as “You are a professional logic validator”) and then using a “user prompt” to 传递 dynamic parameters, a programming-style combination of “system instructions + dynamic parameters” is formed.
- However, the challenge lies in: how to balance standardization and flexibility? For example, “object-oriented” programming requires strict class definitions, while prompts may need more flexible rules to adapt to different scenarios. Perhaps we can draw on the idea of “design patterns” to summarize typical prompt frameworks such as “summary mode,” “dialogue mode,” and “reasoning mode” for users to combine and call according to needs.
- The ultimate goal is to shift prompt writing from “experience-driven” to “rule-driven.” Just as programming evolved from machine language to high-level languages, the threshold for use is reduced through an abstraction layer, and the stability and reusability of prompts are improved through structured design.
- The role-playing feature of ChatGPT:High-quality prompts often define a role with sentence patterns like “I want you to act as xxx.” By providing role descriptions, behavioral rules, and skill descriptions, the model can accurately simulate the behavioral logic of the corresponding role.
- From programming objects to prompt role abstraction:If you are familiar with the concepts of “classes” and “objects” in programming languages, you will find that the “role declaration” of prompts is highly similar to class declarations. Therefore, prompts can be abstracted into a “Role,” whose structure includes:
- Based on this abstraction, LangGPT has designed a Role template, allowing users to generate structured prompts by filling in information according to the template.
- Expansion of LangGPT’s syntax system:
- LangGPT assistant and ecological co-construction: