The AI Engineering Roadmap I’d Follow If I Started Today
I’d skip 80% of what most people waste time on.
If you’re a software engineer or an ambitious professional, you’ve probably felt it: the job market is tougher, roles are shifting, and everywhere you look people are asking: “Will AI replace us?”
On top of that, we’re being constantly bombarded with new complex frameworks to learn, endless tools, new models each week, and tons of hype.
I get it. It’s stressful.
So you do what anyone would do. You open Google or YouTube and start searching for how to “build AI systems.”
Ten minutes later, you’re drowning in PhD papers, a dozen AI agent frameworks that all sound identical, obscure RAG architectures, and model fine-tuning tutorials. You start to think you’ve missed something. But you probably haven’t. You’ve just entered the noise.
I’ve been building large-scale software systems for 15+ years. If there’s one thing that experience teaches you, it’s that we (engineers) love complexity. We learn the hardest thing first and never get around to building something simple that works. We study multi-agent orchestration before we’ve written one reliable function call. We spend weeks on advanced retrieval strategies before we can build a simple agent.
If I started learning AI today, I’d ignore almost all of that and start with what actually leads to working systems. Not the most impressive path - the most effective one.
Flip the Ratio
Most people spend 80% of their time learning advanced techniques and 20% learning the fundamentals. That’s exactly backwards.
You can build most useful AI systems with basic skills. Businesses don’t need exotic architectures. They need software that solves real problems reliably. Most AI systems are just regular software systems that happen to call LLMs.
Good engineering still matters more than anything else. Secure APIs, solid error handling, logging, testing, observability. The AI part is often a few API calls wrapped in the same principles that have worked for decades. Engineers who understand this build faster and ship more.
1. Software Engineering Basics
If you can’t program confidently, start there. Python is common, but the language matters less than fluency. I work with teams building AI systems in Java. Most providers have a TypeScript SDK. Pick what fits your stack and master it.
Learn Git basics. Clone, commit, push, pull. That’s 90% of daily usage.
Understand REST APIs and network protocols (HTTP, DNS, WebSockets). Most AI systems are just APIs talking to APIs. Get comfortable making requests, handling responses, and dealing with failures.
Then learn what large language models are. Learn what they can and can’t do. Know when they hallucinate. Know when they’re overkill. That understanding will save you hundreds of hours chasing hype.
At this stage, forget frameworks. Build one simple thing that works using basic API calls. Make a request to OpenAI or Anthropic. Handle the response. Log it. Retry when it fails. Track the cost.
Build this: A basic web app that calls an LLM API and returns structured output (like extracting a name and email from text). Add error handling and retry logic. This is the foundation everything else builds on.
2. Frameworks
People rush to frameworks because they get a lot of attention. But you don’t need frameworks to build AI systems. Before you use one, write an agent by hand. Let it call a function or two. Handle errors. Store a little state.
Once you’ve done that, a framework like LangGraph or Pydantic AI will make perfect sense. You’ll see it not as magic but as a time-saver - a layer that handles boilerplate you’ve already written.
Frameworks are useful, but only once you’ve built something without them.
Here’s what a basic agent looks like. It’s about 50 lines of Python that handles the core loop: ask the LLM a question, let it call tools if needed, repeat until you get an answer.
class Agent:
“”“
A minimal AI agent that can use tools.
The loop: Ask LLM → Execute tools if needed → Repeat until done
“”“
def __init__(self, model: str = “gpt-4o-mini”, tools: list = None):
self.client = OpenAI()
self.model = model
self.tools = {tool.__name__: tool for tool in (tools or [])}
self.messages = []
def chat(self, message: str) -> str:
“”“Send a message and get a response.”“”
self.messages.append({”role”: “user”, “content”: message})
for _ in range(5): # Max 5 iterations
response = self.client.chat.completions.create(
model=self.model,
messages=self.messages,
tools=self._get_tool_schemas()
)
message = response.choices[0].message
# No tool calls? We’re done
if not message.tool_calls:
self.messages.append(message)
return message.content
# Execute tools and add results
self.messages.append(message)
for tool_call in message.tool_calls:
result = self._execute_tool(tool_call)
self.messages.append({
“role”: “tool”,
“tool_call_id”: tool_call.id,
“content”: result
})
return “Max iterations reached”Build this: Write your own simple agent from scratch with 1-2 basic tools (web search, weather lookup). Then rebuild it using a framework. Notice what the framework handles for you and what it doesn’t. This is how you actually learn what frameworks do.
3. Search (RAG)
Eventually you’ll need your model to use private data. That’s where retrieval-augmented generation comes in. It sounds complex, but it’s really about search - giving the LLM the right information to solve a problem.
There are many search strategies, and the right one depends on your use case. You could stuff everything into the prompt. You could read individual files and load them into the context window. You could use vector search to find smaller snippets that match your query. You could combine vector and keyword search (hybrid). You could let an agent decide what to retrieve. You could add reranking to sort results by relevance.
There are no perfect answers. Start simple and add complexity only when you need it. Find a way to test your search strategy. The smartest models will fail if they don’t have the information they need.
Using the information below, answer the user’s question.
<CONTEXT>
{retrieved_documents}
</CONTEXT>
Question: {user_question}
If the context doesn’t contain the information needed to answer the question, respond with “I don’t have enough information to answer that.” Do not use information outside of the provided context.
Answer:Build this: A Q&A system for your team’s important information. Try just putting everything in the prompt. When that breaks, add basic search. Measure accuracy at each step. This teaches you when complexity is actually needed.
4. Testing (Evals)
Testing AI systems is fundamentally different from testing traditional software. When you build a function that adds two numbers, you know exactly what output to expect. When you build a system that answers customer questions or generates marketing copy, the “right” answer isn’t always clear. The model might respond differently each time, and multiple responses could be equally valid.
There are three main approaches:
Unit tests check the properties that must always be true. Does the output contain required fields? Is it under the token limit? Does it avoid forbidden words? These are binary checks you can automate.
Manual evaluation (human-as-judge) means you read outputs and decide if they’re good. It’s slow but essential early on. You need to understand what “good” looks like before you can automate testing.
LLM-as-judge uses one model to evaluate another’s output. It’s surprisingly effective and scales better than manual review. The key is creating good evaluation prompts and checking that the judge’s decisions match human judgment.
Start simple with manual evaluation to understand what good looks like. Add unit tests for the properties that matter most. Then scale with LLM judges as your system matures.
Build this: Create an eval dataset with 20 question-answer pairs for your system. Run it against two different prompts. Manually grade the outputs. Add 3 unit tests for properties that matter (format, length, required fields). This is the foundation of reliable AI systems.
5. Operations
The difference between a hobbyist and an engineer is deploying to production. Shipping something small will teach you more than any course.
Log every call. Add tracing (Langfuse is a good option). Track tokens, cost, and latency. Treat every model call as an expensive, unreliable dependency - because it is.
The same old rules apply: validate inputs, sanitize outputs, handle errors. It’s just software. The API calls are just more expensive.
Build this: Deploy one of your earlier projects to production. Add logging for every LLM call (tokens, cost, latency). Set up a simple dashboard. Watch it run for a week. You’ll learn more from one week in production than months of local development.
6. Advanced Architecture
Everyone wants to talk about multi-agent systems. They sound cool and make for good conference talks. But most real systems don’t need them. When you do need them, you’ll know why - and you’ll be ready.
Long-running agents, event-driven systems, async patterns - all useful, but not first. Learn the simple path deeply, and the complex one will feel obvious later. Try to learn both at once, and you’ll stall.
The Path Forward
If you follow this roadmap, you’ll be building real AI systems in a few months, not years. You don’t need to read research papers or chase every new framework. You need strong fundamentals and a lot of real-world feedback.
That’s how you actually learn AI engineering: by building things.
Want to learn alongside people doing the same? I run a community where we build real projects together and share what’s actually working in production.
👉 Join the AI Engineering Community
Thanks for reading.
Have an awesome week : )