Top Skills Every Software Engineer Should Learn in 2025

Alright, 2025 is right around the corner, and let me tell you—if you’re not already using LLM SDKs, serverless functions, and vector databases, you’re going to miss out on some serious career gains.

Tech Stack

According to the latest Stack Overflow 2024 survey,

React , Node.js are the most used web technology with 41.6% and 40.7% of professional developers indicating they have done extensive development work with these tools over the past year and expressing a desire to continue using them in the next year.

So if you’re looking for a job in software engineering, you should definitely keep these on your radar.

It's important to note that these are the most popular technologies, but not the only ones. each technology have its own ecosystem and community, and it's essential to keep an eye on the latest trends and advancements in each of them.

Prompt Engineering Concepts

One year ago, I wrote a guide on prompt engineering.

Initially, I created it for my ex-colleagues, who weren’t technical and struggled to make the most out of ChatGPT for their day-to-day tasks.

The guide helped bridge that gap by simplifying concepts and showing practical examples.

Understanding the fundamentals of prompt engineering is crucial when working with LLMs.

This includes knowing:

What keywords to use: Choose the right language to elicit accurate and relevant responses.
How to structure your prompts: Organize your inputs to maximize clarity and context for the AI.
How to use prompts effectively: Tailor them to specific use cases, whether for automation, content generation, or problem-solving.

As a software engineer, you’ll need to be proficient in these concepts to effectively integrate LLMs into your projects.

Open AI SDK: Your New Best Friend

Let’s talk about LLMs SDKs first.

These SDKs are your new best friend.

If you’ve been living under a rock, LLMs (think OpenAI's ChatGPT, Anthropic's Claude, Google PaLM) are the brains behind everything from chatbots to code-generating AIs.

APIs for these models are becoming more similar across providers.

here's a simple example of how to use the OpenAI SDK to generate text:

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

const response = await openai.chat.completions.create({
  model: "gpt-4o", // model is the name of the LLM you want to use
  messages: [
    {
      role: "system",
      content:
        "You will be provided with statements, and your task is to convert them to standard English.",
    },
    {
      role: "user",
      content: "She no went to the market.",
    },
  ],
  temperature: 1, // temperature is a value between 0 and 1, where 0 is the most conservative and 1 is the most creative
  max_tokens: 256, // max_tokens is the maximum number of tokens to generate
});

Get comfortable with one, like OpenAI’s SDK, and it’ll be a breeze to work with others.

Simple, right?

Why It Matters

As more companies turn to AI for everything from customer support to content generation, the ability to quickly plug into any of these APIs and create smart features.

Serverless Functions: The Key to Scalable Apps

But don’t get too comfy just yet, because serverless functions are also a must-learn for 2025.

These are the way to go for building scalable apps without the hassle of managing infrastructure.

With serverless, you're not worrying about servers, VMs, or scaling issues—AWS Lambda, Google Cloud Functions, or Azure Functions just run your code when it’s needed and scale it up without you lifting a finger.

Combining LLMs and Serverless

Here’s where it gets interesting:

combining LLMs with serverless functions is where the magic happens.

Think about it—you’ve got a lightweight function running on the cloud that hits an LLM API, processes the response, and sends it back to the user, all while staying cost-effective and super fast.

Endless Possibilities

Build real-time language processing apps
Automate workflows
Power chatbots at scale

All while saving money and time. Who doesn’t want that?

Vector Databases

Now, let’s talk about vector databases like Pinecone or Postgres with its pgVector extension for AI applications that require fast, scalable search and retrieval.

Why ?

Well, don’t get scared—vector databases are just a fancy way of saying a database that stores data as vectors or multidimensional arrays.

Think really BIG arrays.

These arrays are simply a representation of information in a way that the LLM can understand.

They’re especially useful when you want to feed your data to an AI model.

Use Cases

Vector databases store and search data as vectors, making it easier and faster to perform similarity searches.

This is perfect for use cases like:

Recommendation systems
Semantic search
Document retrieval

Real-World Integration

Imagine combining a vector database with your LLMs and serverless functions.

You could create apps that not only generate intelligent responses but also retrieve the most relevant information from massive datasets in real time.

Tools like Pinecone make it simple to integrate vector search with your applications, scaling as your data grows, without worrying about performance bottlenecks.

Cost Management For Serverless Services

You’ve probably heard of the startup that faced a $30,356.56 Firebase bill or Cara's shocking $96,280 Vercel bill.

These aren’t isolated incidents.

A quick reddit search will reveal countless startups blindsided by unexpectedly high costs overnight.

As we transition to the era of serverless functions, it’s crucial to understand how these systems work under the hood to effectively manage costs.

Take Firebase Cloud Functions as an example:

The expenses aren’t just about the function itself; they also include CPU compute, memory costs.

Since Firebase relies on Cloud Run to execute the code, these hidden costs often go unnoticed.

Worse yet, they’re not even explicitly mentioned in the pricing table.

Developers typically assume they’re covered by the 2M free requests per month—only to get hit with a surprise bill.

Why Does This Matter for 2025?

Here’s the thing: LLM SDKs, serverless functions, and vector databases are not just buzzwords.

These are tools that will unlock new levels of efficiency, scalability, and even creativity in your projects.

As AI continues to evolve, the need to integrate it into your apps seamlessly will be essential. Being able to do that with minimal overhead and maximum performance is going to set you apart from other developers who haven’t caught up yet.

Final Thoughts: Level Up for 2025

Here’s the bottom line: mastering LLM SDKs, serverless functions, and vector databases will give you the edge in 2025.

Dive into one of those SDKs.
Build a few small projects (Like AI Powered nth Todo List)
Test out how serverless functions and vector databases can cut your costs and boost your app's performance.

Once you get a taste of how these tools can level up your apps, you won’t look back.

Don’t wait for someone else to take the lead.

Get in there and start building the future, today.

Time to make 2025 your year.

Let’s get coding! 🚀

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