It wasn't the problem we actually had.
![AI solving the wrong problem]
There is something slightly funny about working with AI today.
We have incredibly capable models. They can write code, analyze data, summarize documents, reason through problems, call tools, and even take actions on our behalf.
So naturally, when something doesn't work, our first instinct is often:
“Maybe we need a better model.”
Then we try a bigger model.
Still not working?
“Maybe we need an agent.”
Still messy?
“Let's add RAG.”
Still not quite there?
“Maybe we need more tools, memory, or a more complicated pipeline.”
And before we know it, we've built a very sophisticated system...
for the wrong problem. 😅
The AI system can be technically correct and still be wrong
This is something I've been thinking about more while learning about AI-native systems.
Imagine asking an AI system:
“Help me reduce customer support response time.”
And it comes back with a beautifully designed solution for improving customer acquisition.
The solution might be:
- logically sound
- technically impressive
- well researched
- perfectly implemented
But it doesn't matter.
It solved a different problem.
This distinction is easy to miss because modern AI is extremely good at producing convincing outputs.
A bad answer used to look bad.
Now, a bad answer can look professional.
That's a much more interesting engineering problem.
Intelligence isn't the same as direction
A highly capable model still needs the right direction.
Think about a very smart employee.
You don't just give them access to every company document, every software tool, and the freedom to make decisions and say:
“Go do something useful.”
You first explain:
- what we're trying to achieve,
- what information matters,
- what they're allowed to do,
- what tools they should use,
- and how we'll know whether the work was successful.
AI systems need similar thinking.
The model is only one component.
The surrounding system determines how that intelligence is actually used.
This is where things get interesting
When building AI applications, it's tempting to start with technology.
For example:
“Which model should we use?”
But perhaps a better starting point is:
“What problem are we actually trying to solve?”
Then we can work backwards.
Do we need external information?
→ Maybe we need retrieval.
Does the system actually need to take multiple steps or interact with tools?
→ Maybe we need an agent.
What kind of reasoning or generation is required?
→ Now we can think about the appropriate model.
And once the system produces an answer or takes an action:
→ How do we know it worked?
That's where evaluation, verification and proof become important.
More components don't automatically mean a better system
This is probably one of the easiest traps to fall into with modern AI.
Because there are so many interesting technologies available, we can start adding them simply because we can.
Need a chatbot?
Add RAG.
Need RAG?
Add a vector database.
Need more flexibility?
Add an agent.
Need the agent to do more?
Add tools.
Need better reasoning?
Use a bigger model.
Need reliability?
Add another layer.
Eventually, the architecture looks incredibly sophisticated.
But complexity should have a reason.
Every component should solve a problem.
Otherwise, we're just making the system harder to understand, maintain and debug.
The model isn't always the problem
Suppose an AI application is giving poor answers.
There are many possible reasons.
Maybe the model doesn't have the information it needs.
Maybe retrieval is returning irrelevant context.
Maybe the context is too large or poorly structured.
Maybe the agent is choosing the wrong tool.
Maybe we're using an unnecessarily expensive model for a simple task.
Maybe our evaluation process isn't catching failures.
Or maybe...
we simply misunderstood the original problem.
Replacing the model might fix none of these.
This is why I think AI engineering is becoming less about “which model are you using?” and more about how the entire system is designed around the problem.
A useful mental model: Retrieve → Agents → Models → Proof
One framework I've been exploring through RAMP is:
Retrieve → Agents → Models → Proof
I like this because it encourages thinking beyond the model itself.
Retrieve
Give the system the information it actually needs.
The goal isn't simply to retrieve more information.
It's to retrieve relevant information.
Agents
If the problem genuinely requires actions, decisions, or multiple steps, then agents can become useful.
But not every problem needs an agent.
Sometimes a simple workflow is better.
Models
Choose the model based on the actual requirement.
The biggest or newest model isn't automatically the right choice.
Sometimes speed matters more.
Sometimes cost matters.
Sometimes reasoning capability matters.
Sometimes a smaller model is perfectly sufficient.
Proof
Finally:
How do we know the system worked?
This is arguably one of the most important questions as AI systems become more autonomous.
An answer isn't automatically trustworthy just because it sounds convincing.
We need ways to evaluate, verify and measure system behavior.
The real goal isn't “make AI smarter”
Of course, better models matter.
Model capabilities have improved dramatically, and they will continue to improve.
But there's another layer to the problem.
A smarter model can produce a better answer to the wrong question.
And that's still the wrong answer.
So perhaps the goal shouldn't simply be:
Make AI smarter.
It should be:
Build systems that use intelligence in the right direction.
That means understanding the problem first, choosing the right architecture second, and selecting the technology that actually supports it.
One question I'm trying to ask more often
Whenever I see a new AI technique, framework or model, instead of immediately asking:
“How can I use this?”
I'm trying to ask:
“What problem would this actually solve?”
That small change in perspective can make a surprisingly big difference.
Because the AI ecosystem is moving extremely fast.
There will always be another model.
Another framework.
Another agent architecture.
Another tool.
Another technique that everyone is talking about.
We probably don't need to use all of them.
We need to understand why we'd use them.
The takeaway
AI can be incredibly smart.
It can reason.
It can retrieve information.
It can use tools.
It can write code.
It can take actions.
But none of that guarantees that we're solving the right problem.
So before asking:
“Which model should I use?”
Maybe ask:
“What exactly am I trying to solve?”
Because sometimes the best AI solution isn't a bigger model.
It's realizing that the problem was defined incorrectly in the first place.
And honestly...
“Cool. But that's not what I asked.”
might become one of the most important debugging messages in AI engineering. 😄
I'm currently exploring these ideas as part of my RAMP learning journey, and this shift—from thinking primarily about models to thinking about the complete AI system—is one of the perspectives I've found particularly useful.
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