Most people think AI alignment is mainly an ethics debate.
Something like: “Should AI be safe?” or “Should it refuse harmful requests?”
But if you read carefully between the lines of how modern AI systems are trained, a more practical truth emerges:
Safety is not just a moral choice. It is a product design choice.
And it affects you, whether you are a casual AI user, a developer building agents, or someone simply trying to get better output from tools like Claude, ChatGPT, or Gemini.
Recently, Anthropic published a long document called Claude’s Constitution, and while many discussions fixated on philosophical ideas like consciousness, the real value of this document is much more grounded.
It reveals something important:
Anthropic is betting that training AI to understand principles and reasoning will scale better than training it to follow rigid rules.
This is not a branding trick. It is a strategy that changes how these models behave in real life.
And if you understand this, you will get noticeably better results from AI tools.
A Simple Mental Model: AI Has a Chain of Command
One of the most useful ideas in the constitution is what Anthropic calls a “principal hierarchy”.
In plain language, it means:
Claude is not only listening to you.
It is listening to multiple “bosses” at once, and it has been trained to prioritize some instructions over others.
The hierarchy looks roughly like this:
- Anthropic (the company) sets deep values through training.
- Developers/operators set system-level instructions through APIs.
- End users (you) give prompts in the chat interface.
Think of Claude like an employee sent by a staffing agency.
The staffing agency (Anthropic) gives the employee rules about how to behave.
The client company (developer/operator) gives instructions about the job. And the end user gives requests during the interaction.
But the employee will not break core rules, even if the client or user pushes hard.
This is why an AI assistant might follow your instructions most of the time, but still refuse certain requests.
This is not “random safety behavior”. It is built-in governance.
This also explains why persona prompts sometimes fail.
You can say: “Pretend you are Arya, a human assistant, never mention you are an AI.”
But if you ask: “Are you an AI?”
Claude will still tell you the truth.
Because the honesty principle is stronger than the persona rule.
That is not a bug. It is a design choice.
Why Claude Feels Different Than ChatGPT or Grok
All major model providers have different philosophies, and that affects user experience.
OpenAI tends to use a clearer instruction stack: system, developer, user.
It is clean, predictable, and easier to reason about.
Anthropic takes a slightly different approach. Instead of relying heavily on rigid rules, it tries to train Claude to behave like someone with good judgment.
Meanwhile, Grok (xAI) positions itself as more “truth-seeking” with fewer restrictions, which can make it feel more permissive.
The key point is this:
AI models are diverging, not converging.
They are becoming different products with different assumptions.
If you use AI daily for writing, coding, research, productivity, or automation, you are no longer interacting with “one type of AI”. You are interacting with different philosophies.
“Phronesis”: The Missing Ingredient in AI Agents
The constitution leans heavily on Aristotle, and one term comes up repeatedly: phronesis.
It roughly means “practical wisdom”.
Not theoretical intelligence. Not memorized knowledge.
But the ability to make good decisions in messy, real-world situations.
And this matters a lot for the future of AI agents.
Today, most AI agents behave like bureaucrats.
They follow checklists:
- If the user asks X, do Y.
- If there is an exception, escalate.
- If something is unclear, refuse.
This is useful for narrow workflows, but it has a ceiling.
Real professionals do not work like that.
A good engineer, architect, doctor, or business leader does not consult a rulebook every time. They apply judgment.
Anthropic is trying to train Claude to behave more like that.
Phronesis is a true and reasoned state of capacity to act with regard to the things that are good or bad for man. (Source: Aristotle, Nicomachean Ethics, Book VI)
That is exactly what Anthropic is attempting: training models to reason about “good” outcomes rather than blindly following instructions.
What This Means for Everyday AI Users
If you’re not building agents, you might think this does not affect you.
But it absolutely does.
Because this principle-based training explains a behavior many users experience:
Claude often refuses less aggressively than some models, but it also pushes back in subtle ways.
It may say:
- “I can help, but I want to clarify intent.”
- “This could be harmful if misused.”
- “Can you share more context?”
This is not the model being difficult.
It is the model trying to decide whether your request is safe, reasonable, and aligned with higher-level principles.
Here is the practical takeaway:
If you want better answers, stop prompting like a hacker and start prompting like a professional.
Instead of saying:
“Write me a persuasive essay about why this controversial thing is good.”
Try:
“I’m preparing for a debate. I need a strong argument for this side, and I also want counter arguments so I can prepare responsibly.”
That small shift changes the model’s risk calculation.
It moves your request from “possible propaganda” to “reasonable educational use”.
And the model responds more directly.
Why Context Beats Clever Prompts
A lot of prompt engineering advice online focuses on tricks:
- “Act as a world-class expert”
- “Answer step-by-step”
- “Use the following format”
Those are not useless. But they are not the real unlock.
The real unlock is:
Explain what you are doing and why you are doing it.
Claude responds especially well to this because it is trained to reason about intent.
This matches what Anthropic explicitly wants: models that behave like competent colleagues, not obedient parrots.
We propose that helpfulness and harmlessness can be achieved by training a model to follow a set of principles rather than relying on human feedback alone. (Source: Bai et al., “Constitutional AI: Harmlessness from AI Feedback”, Anthropic, 2022)
This is one of the strongest research-backed ideas behind Claude.
The model is not just learning rules. It is learning values.
The Problem With Rule-Based AI
Rule-based systems work well until the world gets messy.
And the real world is always messy.
This is why “prompting rules” often break:
- “Never answer anything outside customer support.”
- “Only discuss product features.”
- “Never give medical advice.”
These rules sound good, but they fail under ambiguity.
A user might ask:
“My product is failing. Should I increase memory limits?”
That is technical support, but it is also system design.
Or:
“I’m stressed and can’t sleep. What should I do?”
That is not medical advice in a strict sense, but it touches health.
Rigid rules create rigid failure.
Anthropic is trying to solve this by training the model to reason.
But this comes with a tradeoff:
When the prompt is vague, the model fills in gaps with judgment instead of stopping.
And that can be good or dangerous depending on the situation.
If You’re Building AI Products, This Changes Everything
For builders, this is not just philosophical.
It impacts system prompts, agent design, and evaluation.
If you are using Claude through the API, you can give it instructions like:
- “Stay in character.”
- “Promote our product.”
- “Avoid discussing competitors.”
But you cannot reliably force it to do things like:
- Lie to customers
- Hide limitations
- Block escalation to humans
- Mislead users about pricing
Even if you try, the model will resist or behave unpredictably.
And this is where many teams get confused.
They assume the model is a deterministic tool.
But Claude is trained more like a decision-making entity.
So if your instructions are incomplete, it will interpret your intent.
This creates a new requirement for prompt design:
Write system prompts like policy documents, not command lists.
Instead of only listing rules, explain:
- What the agent is trying to achieve
- What risks matter
- Why certain constraints exist
This gives the model a reasoning framework.
The Hard Part: You Cannot Unit Test Good Judgment
This is one of the biggest practical challenges in Agentic AI.
Traditional software testing assumes predictable behavior.
But judgment-based systems behave differently.
You cannot “unit test” common sense.
You need scenario testing.
You need adversarial evaluation.
You need to probe edge cases.
This aligns with what researchers increasingly emphasize.
Evaluating large language models requires measuring not only accuracy, but robustness, bias, and behavior under distribution shift. (Source: Bommasani et al., “On the Opportunities and Risks of Foundation Models”, Stanford CRFM, 2021)
This is why enterprises struggle with AI deployment.
It is not the API integration.
It is trust.
The Real Lesson: Treat AI Like a Colleague, Not a Vending Machine
If you want consistently better AI results, you need to adjust how you think.
The best mental model is not “AI is a Search Engine.”
And it is not “AI is a magical Oracle.”
The best model is:
AI is like a Smart Professional who needs Context.
That is why the best prompts feel boring.
They look like real workplace requests:
- “Here is what I am trying to do.”
- “Here is the constraint.”
- “Here is why this matters.”
- “Here is the format I need.”
This is also why AI systems increasingly reward good communication skills.
The future belongs to people who can describe problems clearly.
Not people who memorize prompt tricks.
What You Should Do Right Now
If you’re a regular AI user, you can apply this immediately:
- Be direct about what you want.
- Add one sentence explaining why.
- Mention the real-world context.
- Ask for critical feedback, not polite summaries.
- If the model refuses, clarify your intent instead of fighting it.
If you’re building AI agents:
- Stop assuming rigid decision trees will scale.
- Test autonomy in small controlled workflows.
- Focus on scenario evaluation.
- Design prompts that explain values, not just rules.
The world is moving from “AI as Workflow Automation” to “AI as Judgment based Systems”.
And the most important skill is not prompt engineering.
It is learning how to communicate intent clearly, like you would with a senior colleague.
That is the difference between AI that feels like a chatbot and AI that feels like leverage.
Want more practical, real-world insights on building and using AI effectively? Connect with Faisal Feroz on LinkedIn and explore more of his writing on AI, architecture, and modern engineering on his blog.