Many people have recently had a subtle experience: AI models have obviously become more powerful, but using them has become increasingly 'awkward'.
You have likely encountered such situations as well:
Having back-and-forth conversations with AI for dozens of rounds
Making small adjustments to the code and adding conditions
Clearly just implementing a function, yet adjusting multiple times
Ultimately, the token cost is very high, yet the results are still unstable
This seems very unreasonable. The model's capabilities are rapidly improving, why hasn’t the efficiency of usage increased in sync?
Anthropic's recent release of usage recommendations for Claude Code with Opus 4.7 explains this phenomenon perfectly. But if you only treat it as an ordinary user guide, you will miss its most core value.
Because the advice behind this reveals not just simple usage techniques, but a fundamental shift:
AI programming is transitioning from the 'dialogue generation' era to the 'task management' era.
This is not a prompt optimization guide, but a redefinition of the collaboration relationship between humans and AI.
From 'reactive assistant' to 'delegated engineer'.
In the past two years, most people have gotten used to treating AI as an enhancement tool:
A search engine that writes code.
A smarter Stack Overflow.
A Copilot that can chat anytime.
A typical usage method is 'iterative approximation': throw out a question, see the answer, add conditions, modify, and gradually approach the desired result.
This approach was very effective in the early days of ChatGPT because back then the model was more like a reactive assistant – helping you fill in a code snippet, explain an error, or modify a function.
However, the new generation of tools represented by Claude Code + Opus 4.7 is changing the structure of tasks that models are good at.
The core advice given by Anthropic can be distilled into one sentence:
Don't treat the model as a pair programming partner, but as an engineer to whom you delegate tasks.
The meaning of this sentence is very profound:
The interaction unit has changed from 'single response' to 'complete task'.
The evaluation standard has shifted from 'whether the answer is correct' to 'whether the task is successfully delivered'.
The role of the user has changed from 'guidor' to 'task definers + acceptors'.
This is no longer an optimization at the prompt level, but a reconstruction of the human-machine collaboration relationship.
Why is multi-round dialogue becoming inefficient?
Many people know that 'fewer rounds save tokens', but that is just a superficial reason. The real root cause lies in the change of the model's cost structure.
In early models, each round of dialogue was basically a simple generation with limited reasoning depth.
In advanced models like Opus 4.7, each additional round of dialogue may contain:
Reconstruction of task understanding.
Realignment of context.
Parsing of constraint conditions.
Solution planning.
Decision-making for tool invocation.
In other words, each round is not just about saying one more sentence, but about performing a completely new task modeling.
The cost of multi-round interactions no longer increases linearly but is the accumulation of repeated modeling. This directly leads to the past habit of 'trying first, then adding a bit, then modifying' becoming costly, slow, and unstable with the new generation of agent-type models.
Therefore, Anthropic repeatedly emphasizes that the first round should be:
Clarify the task.
Provide complete context.
Clarify all constraints.
Clearly write the acceptance criteria.
Because the most expensive part is not content generation, but repeatedly reconstructing the problem itself.
The upgrade of prompts: from questioning techniques to task specifications.
The core competency in the previous phase was Prompt Engineering. Now, an important upgrade is taking place.
Prompts are evolving into Specifications (task specifications).
In the past, writing prompts was mainly about optimizing expression to help the model better understand the problem.
When writing a prompt now, it is about defining a task that can be reliably executed, which needs to be clearly included.
What is the goal?
Where are the boundaries?
What resources can be used?
What counts as completion?
This is very similar to PRD, technical schemes, and acceptance criteria in software engineering.
The important change brought about is that being able to write prompts is no longer just a language skill, but a system design skill.
What’s truly critical is no longer 'how you ask', but:
Can you clearly define the problem?
Can you break down the goals and constraints?
Can you provide just the right context?
Can you design acceptance criteria in advance?
Therefore, we can make a stronger judgment:
The next phase of AI programming is not Prompt Engineering, but Specification Engineering.
Adaptive thinking: models begin to autonomously manage reasoning resources.
Opus 4.7 has canceled fixed thinking budgets and switched to adaptive thinking.
This change seems to be a technical detail, but it is significant: the model is starting to manage reasoning resources on its own, rather than being assigned by humans.
In the past, humans decided how long to think about a problem; now, the model makes that judgment autonomously.
Whether deep reasoning is needed.
To what extent should reasoning be carried out.
Whether it's worth investing more computation.
This means that the focus of model capability has shifted: it is no longer just about 'can it reason', but 'will it intelligently judge when to reason, how to control depth, and balance speed, cost, and accuracy'.
The way humans control will also upgrade, shifting from parameter control (budget, steps) to strategy control (intent, preferences), for example:
"This problem is quite complex, please reason step by step."
"Prioritize a quick reply, no need for in-depth analysis."
Human-machine interfaces are transitioning from bottom-level parameters to high-level strategies.
Why has the model suddenly become 'conservative'?
Many users have observed: tool invocation has decreased, sub-agents have become more cautious, and answers have become shorter.
This is not a regression in capability, but a proactive trade-off in product philosophy by Anthropic — it’s not about making the model do as much as possible, but about getting things done correctly at controllable costs.
There are three underlying goals:
Reduce ineffective executions (radical agents are easily busy but have no actual output).
Increase the predictability of behavior (enterprise users need stability and reliability, not occasional high performance).
Return the intensity of exploration to the user (default conservative, but allow users to explicitly authorize more aggressive behavior).
This is essentially about redefining the boundaries of responsibility: systems are responsible for safety and cost, while users are responsible for task intensity and exploration scope.
The entire industry is shifting towards 'task management'.
Although Claude Code is a product of Anthropic, this direction is actually a common trend across the entire AI industry.
Different companies have different paths, but the underlying competition is about the same thing: who can become the effective scheduling layer between the model and real work.
Anthropic defines 'AI engineer' through Claude Code.
OpenAI strengthens tool invocation and general agent capabilities.
Google deeply integrates Gemini into the entire Workspace suite.
Cursor completely integrates AI into developer IDEs.
Devin attempts end-to-end automation of software tasks.
On the surface, it’s a difference in product form, but fundamentally it’s a competition of capabilities in task reception, context understanding, step planning, tool invocation, and result verification across the entire chain.
The model is the engine, while the tool layer and workflows are the real transmission system. The future's winners and losers will likely depend on the quality of this transmission system.
Anthropic vs Google: Agent vs Environment.
Broaden the vision; different companies are betting on different futures:
Anthropic defines an Agent: to enhance the task execution capability of individual intelligent agents, allowing users to confidently delegate tasks to AI.
Google is defining the environment: not as an isolated agent, but embedding AI into every existing work node of the user (Gmail, Docs, Sheets, Drive, etc.).
In summary:
Anthropic lets you delegate tasks to AI, while Google makes AI part of every step in your work.
What truly matters is the human-machine division of labor model.
Many discussions remain at the level of 'who is smarter, who has a longer context, who is faster', but the more essential difference lies in the default human-machine division of labor model of each product.
Claude Code: humans define tasks, AI executes and delivers.
ChatGPT: humans and AI jointly explore problems.
Cursor: humans lead development, AI provides acceleration.
Gemini: humans do not change existing processes, AI is embedded in processes.
Devin: AI replaces the entire process as much as possible.
The real difference is not the capability of the model, but the level at which humans participate in the work has been redefined.
The value of engineers is on the rise.
As AI takes on more and more work at the 'implementation layer', the value of engineers is migrating from basic execution to higher-level tasks.
From
Write code, debug interfaces, debug.
Shift to
Define problems, break down systems, design constraints, control risks, and establish acceptance criteria.
This is a typical upward shift in division of labor.
Large models have not eliminated software engineering, but are rearranging high-value segments within engineering.
Conclusion: This is an upgrade in software production methods.
The true significance of Claude Code + Opus 4.7 is not that it can write code faster or answer questions more intelligently, but that it is driving a deeper transformation:
Software production is moving from 'humans write code, AI assists' to 'humans define systems, AI executes implementations'.
When this trend is established, many things will change accordingly:
Prompts will evolve into Specifications.
Dialogue will transform into task delegation.
Tools will upgrade to workflows.
Engineers will transform into Orchestrators (system orchestrators).
What this guide really wants to convey is not 'how to use Claude more efficiently', but:
Future software is not written, but defined.