Context Engineering is a very important concept in AI application development in the past two years. It can be understood as:

How to construct the "most appropriate context" for a large model so that it can output better results.

Simply put:

Prompt Engineering is to write prompt words, and Context Engineering is to design the entire input context system.

Its scope is much larger than Prompt.

1. Why Context Engineering is needed

Large models have a core limitation:

It can only think based on the context of the current input.

That is:

LLM 思考能力 = Prompt + Context

If the context is bad, this will appear:

• hallucination
• misunderstanding
• Forgetting long conversations
• Output is unstable

So the real engineering problem for AI systems is:

How to construct high-quality context.

This is Context Engineering.

2. What is usually included in Context?

The Context of a mature AI system generally includes 6 parts:

┌───────────────────────┐
│ System Prompt         │
│ 用户输入               │
│ 历史对话               │
│ 检索知识（RAG）        │
│ 工具结果               │
│ Agent思考过程          │
└───────────────────────┘

A little more complete:

Context =
System Prompt
+ Conversation History
+ Retrieved Knowledge
+ Tool Outputs
+ Memory
+ User Input

3. Core technologies of Context Engineering

Typically contains 6 key technology modules.

1 prompt design

The most basic layer.

For example:

You are a senior software architect.
Answer with concise technical explanations.

Or structured prompt:

Role:
Task:
Constraints:
Output format:

But Prompt is only for Context part 。

2 RAG (knowledge injection)

Add external knowledge to the Context.

Process:

用户问题
 ↓
向量检索
 ↓
相关文档
 ↓
拼接进Prompt

Example:

Context:
[文档1]
[文档2]

Question:
xxx

This way the model can answer private knowledge.

3 Conversation Memory

History needs to be remembered during long conversations.

Common ways:

Sliding Window

Only the most recent N rounds of conversations are retained.

Last 10 messages

Summarization Memory

To summarize the old conversation:

Conversation Summary:
用户正在开发React项目...

Vector Memory

Historical information is vectorized and retrieved on demand.

4 Tool Context

After the Agent calls the tool, it needs to add the result to the Context.

For example:

Tool: weather_api
Result: Beijing 22°C

Let LLM continue reasoning.

5 Token Budget Management

LLM has Context Window Limitation.

For example:

• 8k
• 32k
• 128k
• 1M tokens

Context Engineering needs to do:

• Document slicing
• Remove duplicates
• Summary
• relevance ranking

For example:

top_k = 5
max_tokens = 2000

6 Structured Context

Make the Context more structured.

For example:

JSON format:

{
  "user_question": "...",
  "retrieved_docs": [],
  "history": [],
  "tool_results": []
}

Structuring improves stability.

4. Context Engineering architecture

A complete AI system is usually:

                User Input
                     │
                     ▼
             Context Builder
        ┌─────────────────────┐
        │ Prompt Template      │
        │ RAG Retrieval        │
        │ Conversation Memory  │
        │ Tool Results         │
        │ System Rules         │
        └─────────────────────┘
                     │
                     ▼
                    LLM
                     │
                     ▼
                  Response

Core components:

Context Builder

5. Give a real AI Chat example

User asked:

如何优化React性能？

Context Builder construct:

System:
You are a senior frontend architect.

History:
用户正在开发大型React系统。

RAG:
[React性能优化文档]

User:
如何优化React性能？

LLM just started to answer.

6. Why Context Engineering is important

Because LLM itself cannot control the output quality.

The only way to control quality is to:

Control input context.

This is the core of AI engineering.

7. Context Engineering vs Prompt Engineering

Comparison	Prompt Engineering	Context Engineering
Scope	Writing Prompt Words	Designing Contextual Systems
Contains	prompt	prompt + RAG + memory
Does it involve architecture	No	Yes
Is it engineered	Low	High

So many AI engineers say:

Prompt Engineering is obsolete, Context Engineering is the real AI engineering.

8. Current basic architecture of AI system

Almost all AI products are:

Agent
  │
  ▼
Context Engineering
  │
  ▼
LLM

Context Engineering is responsible for:

• Information input
• memory
• knowledge
• Tool results

9. What do front-end engineers need to do?

In the AI Chat / Copilot system, the front end often needs to participate in:

1 Context splicing

For example:

messages = [
 systemPrompt,
 history,
 ragDocs,
 userMessage
]

2 Token management

Avoid making the Context too large.

3 streaming output

SSE / WebSocket。

4 Session management

Save conversation.

10. What is Context Engineering?

Context Engineering is a key technology in AI application development, which refers to improving the quality of generation by designing and managing contextual information input to large models.

It includes not only Prompt design, but also RAG retrieval, dialogue memory management, tool call results, Token Budget control, etc.

In actual AI systems, user input, historical conversations, retrieval knowledge, and tool results are usually combined into the final Context through Context Builder, which is then sent to the large model for reasoning.