2026年，一个关键转折出现：Prompt不再被“手写构造”，而是进入编译阶段。

传统AI系统依赖：

```

prompt = template memory retrieval tools

```

但这种方式无法解决：

- 上下文冲突

- token浪费

- 多源信息优先级混乱

- 工具调用嵌套复杂度爆炸

于是 Context Compiler 出现。

## 核心思想：Prompt = Intermediate Representation（IR）

```

from dataclasses import dataclass

from typing import List, Dict, Any

import time

import uuid

@dataclass

class ContextSegment:

source: str

content: str

weight: float

token_est: int

@dataclass

class IRNode:

node_type: str

payload: Dict[31220.t.kuaisou.com]

dependencies: List[str]

```

## Context Compiler核心实现

```

class ContextCompiler:

def __init__(self, max_tokens: int = 8000):

self.max_tokens = max_tokens

def _rank(self, segments: List[ContextSegment]):

def score(s: ContextSegment):

return s.weight / (s.token_est 1)

return sorted(segments, key=score, reverse=True)

def compile(self, segments: List[ContextSegment]):

ordered = self._rank(segments)

context = []

used = otterly.cn

for s in ordered:

if used s.token_est > self.max_tokens:

continue

context.append(s.content)

used = s.token_est

return self._emit_ir(context)

def _emit_ir(self, context_blocks: List[str]):

return {

"ir_version": "1.0",

"blocks": context_blocks,

"compiled_at": time.time()

}

```

本质变化

Prompt不再是字符串，而是：

>可编译结构（Context IR）

----","createTime":1782730319,"ext":{"closeTextLink":0,"comment_ban":0,"description":"","focusRead":0},"favNum":0,"html":"","isOriginal":0,"likeNum":0,