Over the past few years, rapid engineering has been one of the most discussed topics in artificial intelligence.
Developers shared message templates.
Companies hired fast engineers.
Social media was filled with examples of carefully crafted prompts designed to produce better AI results.
At first it fascinated me.
Like many developers, I spent countless hours experimenting with prompts. I tried different structures, instructions, formatting techniques, and role-playing methods. Sometimes small changes produced surprisingly different results.
Rapid engineering felt like a superpower.
But the more I worked with AI systems, the more I realized something important:
Quick engineering is useful, but calling functions is what transforms an AI model into a practical software system.
That realization changed the way I build AI applications.
The early days of rapid engineering
When big language models first became widely accessible, prompts were everything.
The developers discovered that writing mattered.
A simple instruction could produce mediocre results, while a carefully crafted prompt could produce significantly better results.
People experimented with techniques like:
- Chain of thought instigation
- Role instruction
- Examples of few shots
- Structured Outbound Messages
- Multi-step instructions
These techniques helped improve reliability and consistency.
For many applications, rapid engineering is still valuable today.
The problem is that the indications alone have limits.
The limitations of rapid engineering
Imagine asking an AI model:
“What is the weather in Lagos right now?”
Without external access, the model cannot reliably provide live information.
Now imagine asking:
“Analyze my database and tell me which products are underperforming.”
Again, the model cannot access the database.
Or maybe:
“Review my website and identify technical SEO issues.”
The model cannot inspect a website on its own.
No matter how sophisticated a message is, the model is still limited by the information available within its context window.
At some point, better directions stop solving the problem.
You need tools.
That’s where function calls come into the picture.
What is function call?
Calling functions allows an AI model to interact with external systems.
Instead of relying exclusively on its internal knowledge, the model can request actions.
These actions could include:
- searching the web
- Call API
- Consult databases
- Reading files
- Sending notifications
- Running calculations
- Activate workflows
Instead of generating responses solely from memory, the model can collect real information before responding.
This dramatically expands what AI applications can achieve.
From text generation to task execution
The biggest difference between quick engineering and function calling is simple.
Rapid engineering improves responses.
Function calls enable actions.
This distinction is critical.
A model that only generates text is still a chatbot.
A model that can use tools becomes an agent.
For example, if a user asks:
“Generate an SEO audit for my website.”
A quickly designed chatbot could provide generic advice.
A quickly designed chatbot could provide generic advice
An AI agent using function calls can:
- Crawl the website.
- Analyze metadata.
- A model that only generates text is still a chatbot.
- Detect technical problems.
- Generate recommendations.
The result is based on real data and not assumptions.
That’s a completely different level of capability.
Because I changed my development approach
When I started building AI applications, most of my effort went into quick optimization.
I spent hours refining instructions.
I adjusted the wording repeatedly
I experimented with countless variations of prompts.
Finally, I noticed something surprising.
The biggest performance improvements didn’t come from quick changes.
They arose from adding tools.
Once I connected APIs, databases, crawlers, and custom functions to the model, the quality of the system improved dramatically.
The model became more useful because it could access information and take actions.
The message mattered.
But the tools mattered more.
Building real AI agents
Today, many developers talk about AI agents.
In my experience, calling functions is one of the foundations that makes agents possible.
An agent typically needs:
- understand the objectives
- Choose tools
- Run actions
- Analyze results
- Continue workflows
Without function calls, these capabilities become extremely difficult to implement effectively.
The model needs a bridge between reasoning and execution.
The function call provides that bridge
It allows intelligence to connect with real-world systems.
Function calls create better architecture
Another reason I value function calls is the architecture of the software.
Prompt engineering often encourages placing more logic within prompts.
This can be difficult to maintain.
Calling functions encourages a different approach.
Instead of incorporating business logic into prompts, developers create dedicated functions.
Each function performs a specific task.
The model decides when those functions should be used.
This creates systems that are:
- Easier to maintain
- Easier to scale
- Easier to test
- Easier to improve
From a software engineering perspective, that’s a huge advantage.
Is rapid engineering still important?
Absolutely.
Rapid engineering is still important.
A poorly designed message can still produce bad results.
Clear instructions, structured results and effective context management continue to play valuable roles.
However, I no longer consider rapid engineering to be the most important skill in AI development.
I see it as a component of a much larger system.
The most powerful AI applications combine:
- Good directions
- function call
- External tools
- Reliable Workflows
- Structured architecture
The magic happens when these pieces work together.
Looking to the future
As AI technology continues to evolve, I believe the industry will focus less on quick tricks and more on system design.
The future is not about finding the perfect message.
It is about building intelligent systems that can reason, collect information, interact with tools and execute tasks.
That’s where function calls shine.
It transforms language models from text generators into software components capable of participating in real workflows.
Final thoughts
Rapid engineering helped introduce developers to the power of large language models.
It is still a valuable skill.
But if I had to choose between mastering rapid engineering and mastering function calling, I would choose function calling every time.
Prompts improve conversations.
Function calls create products.
And, in my experience, the future of AI development belongs to developers who know how to connect intelligence with action.
