The core value of the big model is "programmable general intelligence"
Explore the "programmable general intelligence" of large models and open a new era of AI applications.
Core content:
1. The intelligent process of large models and "temporary thinking loops"
2. The art of programming from "general intelligence" to "directional assistant"
3. The future of AI product structure: intelligent engineering production line
The core value of the big model is "programmable general intelligence"
1. The big model is not a "super search engine" but a software CPU
It can do much more than just information search, but complete a complete set of intelligent processes of "input-understanding-reasoning-generation". The difference is that it does not rely on hard coding, but relies on context + prompt words + tools to form a "temporary thinking loop". This means that you don't need to write an algorithm, you just need to arrange a "thinking path".
2. The essence of all LLM applications is to deploy a piece of "general intelligence"
You are not calling a model, but driving a "brain with language understanding and reasoning capabilities." This "intelligence engine" can:
Read user input and identify the nature of the task; Call external knowledge/tools to complete information; Generate responses or actions that are logically and contextually consistent.
It’s an “invokable brain” rather than an “enhanced search box”.
3. The real “core value” is: programmable
The capability boundary of the big model lies not in the model itself, but in how you combine prompts, knowledge, context, functions and feedback mechanisms . This makes the big model no longer a single-point model, but a "Composable Intelligence Flow" .
Programmable ≠ Multiple prompts Programmable = Clear target → Construct context → Inject tools → Program output → Feedback optimization
4. The so-called "context management" is just the memory layer of this orchestration system
The role of context is to build an "immediate cognitive space" for LLM: it determines what the model knows, what it pays attention to, and what it prefers. However, context does not solve the action structure and goal achievement, which must be completed by "control flow" and "tool orchestration".
Context is the foundation, but what really builds smart processes are:
Prompt template: encapsulate roles, tasks, constraints Dynamic memory: recording long-term information across conversations/sessions Tool call: API/database/function-assisted reasoning Chain/Agent: Forming flowcharts and task chains
5. Arrangement is the bridge between “universality × purposefulness”
You are always faced with:
A general large model; A specific scenario (contract analysis, financial report summary, creative writing); A bunch of user task requirements.
Only through orchestration can you transform a "general brain" into a "directed assistant." In other words: the big model can do everything, but you have to tell it how to think, where to look, and where to go.
6. The structure of all AI products will eventually converge to:
→ This structure is the “engineered production line of intelligence”.
7. The most cost-effective way to start an AI business: replace model training with orchestration
Don’t retrain the model, and don’t just sell prompts. Focus on how to use standard models + private data + tool chains + task chains to form a closed loop of business value. What you want to build is not a “model”, but a “continuously evolving intelligent process”.
Possible scenarios include:
Smart summary/contract extraction/legal assistant Data Analysis/ Table Understanding/ SQL Automation Multi-round dialogue decision-making system Vertical Search and Insight Platform Personalized content generation factory (copywriting, scripts, courses)
8. Long-term barriers are not in the model, but in orchestration × data × feedback
Open source models and APIs will continue to gain popularity, and what really makes the difference is:
Who can build a more efficient prompt/tool flow? Who has a better private domain knowledge base/user portrait? Whose feedback loop runs more accurately and faster (RAG + Auto-Eval + RL)
The essence of competition for large models has shifted from “good training” to “fast adjustment”, “skillful programming” and “accurate use”.
9. Orchestration is not a technical detail, but the soul of the product
Orchestration determines whether the model "calls the right knowledge at the right time to solve the user's problem in the right way". The more you understand the user's goals, the better you can construct high-quality information flow and control flow. This is not an algorithm problem, but a problem of product insight + information structure design ability.
10. The next wave of AI products will be a trinity of “intelligent agent × orchestration engine × scenario closed loop”
Intelligent agents provide task autonomy Orchestration engines provide structure and scheduling Scenario closed loop provides data and feedback
On top of these three, building the "industry-based implementation of general intelligence" is the real way to build a moat.
To sum up in one sentence:
The core value of a big model is not how much it knows, but how much you can make itThink and act in a structured way. Orchestration is the key to connecting model capabilities with business value.
