Context Engineering - a new made-up AGI term | Insights and observations of AGI industry

The core concept and industry impact summary of context engineering is as follows:

1. Definition and Value of Context Engineering

Core Objective: Achieve predictability and stability in AI applications through structured input (e.g., JSON format) and output (e.g., consistent and valuable results), addressing the issue of unpredictable outputs in traditional AI applications.
Key Values:
- Stable Output: Ensure consistent and valuable AI results (e.g., solving specific problems) for every input.
- Reusability: Intermediate results generated via context engineering can be parsed and utilized by subsequent processes, enhancing application efficiency.
- Reduced Error Costs: Avoid repeated validation and correction caused by unstable outputs.

Steps:

Instructions / System Prompt: An initial set of instructions that define the behavior of the model during a conversation, can/should include examples, rules …. User Prompt: Immediate task or question from the user. State / History (short-term Memory): The current conversation, including user and model responses that have led to this moment. Long-Term Memory: Persistent knowledge base, gathered across many prior conversations, containing learned user preferences, summaries of past projects, or facts it has been told to remember for future use. Retrieved Information (RAG): External, up-to-date knowledge, relevant information from documents, databases, or APIs to answer specific questions. Available Tools: Definitions of all the functions or built-in tools it can call (e.g., check_inventory, send_email). Structured Output: Definitions on the format of the model’s response, e.g. a JSON object.

2. Comparison of Large Models’ Support for Context Engineering

Key Conclusions:

Pro-tier models (e.g., GPT-4o, Gemini 2.5 Pro) perform best in context engineering, suitable for complex tasks.
Flash-tier models (e.g., Gemini 2.5 Flash) offer faster speeds but incomplete outputs, ideal for simple scenarios.
Domestic models (e.g., DeepSeek, Qwen3) are increasingly approaching international standards in format output and stability, though there is still room for optimization.

3. Industry Impact and Future Trends

Upgrades for Large Model Providers:
- Extend Context Length: Models like LLAMA4 (1M TOKEN) and Gemini 2.5 (2M TOKEN) already support long-context capabilities.
- Enhance Native Tool Calls: Models like DeepSeek R10528 now support tool calls; further optimization is needed.
- Stabilize JSON Output: Ensure format correctness to avoid repeated or missing fields.
AI Application Explosion:
- From “Unusable” to “Usable”: Context engineering will drive AI applications from experimental stages to practical use.
- Hardware Demand Growth: Long-context processing and large-scale AI applications will intensify demand for hardware like NVIDIA GPUs.
Future for Ordinary Users:
- No Need for Programming: Future large models may enable context engineering via natural language interaction, lowering the usage threshold.
- Developer Opportunities: Developers can rapidly build AI applications using context engineering, seizing market opportunities.

4. Practical Recommendations

Developers:
- Prioritize models supporting 128K+ context and native tool calls (e.g., GPT-4o, Gemini Pro).
- Use structured JSON output to ensure subsequent processes can parse results.
- Optimize application logic using the 4-step context engineering process (structured input → tool calls → intermediate results → final output).
Enterprises/Servicers:
- Accelerate model upgrades to support long-context and tool-call capabilities.
- Establish standardized context engineering workflows to reduce AI application development costs.
Ordinary Users:
- Focus on AI application stability and avoid relying on “eureka” outputs.
- Learn the logic of context engineering (e.g., 6 key components) to understand how AI applications work.

5. Summary

Context engineering is the “universal solution” for AI applications: Structured input and output resolve stability issues in AI applications.
Technical Challenges and Opportunities Coexist: Current models require further optimization, but developers already have the capability to rapidly deploy applications.
Future Trends: Large models will enable context engineering via natural language interaction, driving AI applications from “experimentation” to “practical use.”

Call to Action:

Developers: Immediately learn the context engineering process to seize AI application development opportunities.
Enterprises: Upgrade model capabilities and layout the next generation of AI applications.
Ordinary Users: Focus on AI application stability and understand its underlying logic.

If you need further discussion on specific model selection or application cases, feel free to ask!

Translation

上下文工程的核心概念与行业影响总结

1. 上下文工程的定义与价值

核心目标：通过结构化输入（如JSON格式）和输出（如稳定结果），实现AI应用的可预测性与稳定性，解决传统AI应用输出不可控的问题。
关键价值：
- 稳定输出：确保每次输入都能生成一致的、有价值的AI结果（如解决特定问题）。
- 可复用性：通过上下文工程生成的中间结果，可被后续流程解析和利用，提升应用效率。
- 降低容错成本：避免因输出不稳定导致的反复校验和纠错。

Steps:

Instructions / System Prompt: An initial set of instructions that define the behavior of the model during a conversation, can/should include examples, rules
User Prompt: Immediate task or question from the user.
State / History (short-term Memory): The current conversation, including user and model responses that have led to this moment.
Long-Term Memory: Persistent knowledge base, gathered across many prior conversations, containing learned user preferences, summaries of past projects, or facts it has been told to remember for future use.
Retrieved Information (RAG): External, up-to-date knowledge, relevant information from documents, databases, or APIs to answer specific questions.
Available Tools: Definitions of all the functions or built-in tools it can call (e.g., check_inventory, send_email).
Structured Output: Definitions on the format of the model’s response, e.g. a JSON object.

2. 大模型支持上下文工程的能力对比

| 模型 | 输入长度 | 原生工具调用支持 | JSON格式输出稳定性 | 适用场景 | |——————|————–|———————-|————————-|———————————-| | GPT-4o | 128K+ | ✅ | ✅（95%+正确率） | 复杂任务、高精度需求 | | Gemini 2.5 Pro| 100万TOKEN | ✅ | ✅（95%+正确率） | 大规模数据处理、多步骤推理 | | Claude 3 | 128K+ | ✅ | ✅（95%+正确率） | 企业级应用、多模态任务 | | DeepSeek R10528| 128K | ✅ | ✅（90%+正确率） | 开发者实验、中等复杂任务 | | Mistral | 64K+ | ⚠（部分支持） | ⚠（格式错误率较高） | 简单任务、快速原型开发 | | GROK-3 | 128K+ | ✅ | ⚠（推理过程可能写入JSON）| 初期测试、非结构化输出需求 |

关键结论：

Pro版模型（如GPT-4o、Gemini 2.5 Pro）在上下文工程中表现最佳，适合复杂任务。
Flash版模型（如Gemini 2.5 Flash）速度更快但输出不完整，适合简单场景。
国内模型（如DeepSeek、通义千问3）在格式输出和稳定性上逐渐接近国际水平，但仍有优化空间。

3. 行业影响与未来趋势

大模型服务商的升级方向：
- 延长上下文长度：如LLAMA4（1000万TOKEN）和Gemini 2.5（200万TOKEN）已具备长上下文能力。
- 增强原生工具调用：如DeepSeek R10528已支持工具调用，未来需进一步优化。
- 稳定JSON输出：确保格式正确性，避免重复或缺失字段。
AI应用的爆发：
- 从“不可用”到“可用”：上下文工程的普及将推动AI应用从实验阶段进入实用阶段。
- 硬件需求增长：长上下文处理和大规模AI应用将加剧对英伟达显卡等硬件的需求。
普通用户的未来：
- 无需编程：大模型未来可能通过自然语言交互实现上下文工程，降低使用门槛。
- 开发者机会：当前阶段，程序员可通过上下文工程快速开发AI应用，抢占市场先机。

4. 实践建议

开发者：
- 优先选择支持128K+上下文和原生工具调用的模型（如GPT-4o、Gemini Pro）。
- 使用JSON结构化输出，确保后续流程可解析。
- 通过4步上下文工程流程（输入结构化→工具调用→中间结果→最终输出）优化应用逻辑。
企业/服务商：
- 加速升级模型支持长上下文和工具调用能力。
- 建立标准化的上下文工程流程，降低AI应用开发成本。
普通用户：
- 关注AI应用的稳定性，避免依赖“灵光一现”的输出。
- 通过学习上下文工程逻辑（如6个关键部分）理解AI应用的运作方式。

5. 总结

上下文工程是AI应用的“万能解药”：通过结构化输入和输出，解决AI应用稳定性问题。
技术挑战与机遇并存：当前模型需进一步优化，但开发者已具备快速落地的能力。
未来趋势：大模型将通过自然语言交互实现上下文工程的普及，推动AI应用从“实验”走向“实用”。

行动号召：

开发者：立即学习上下文工程流程，抢占AI应用开发先机。
企业：升级模型能力，布局下一代AI应用。
普通用户：关注AI应用稳定性，理解其背后的逻辑。

如需进一步探讨具体模型选型或应用案例，可继续提问！

Reference:

https://blog.langchain.com/context-engineering-for-agents/,
https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider,
https://www.philschmid.de/context-engineering

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