The Layered Mental Model I Use to Understand Modern AI (As a Software Engineer)

AICamp — Wed, 07 Oct 2026 00:51:39 +0000

If you're a software engineer who understands traditional systems but still finds the massive explosion of AI terminology confusing, you're definitely not alone.

AI, machine learning, neural networks, deep learning, transformers, LLMs, generative AI, and AI agents are often explained as completely separate concepts or marketing buzzwords. The confusing part is that they're actually deeply connected, overlapping layers that build on top of each other.

I put together a beginner-friendly, 12-minute visual whiteboard guide breaking down these 7 layers step-by-step for engineers. You can watch the full walkthrough directly below:

The 7 Layers of the AI Stack

Instead of trying to memorize a chaotic glossary, I've found it much cleaner to map out how these concepts structurally stack together:

AI (The Umbrella): The broad goal of building systems that can do things we normally associate with human intelligence—recognizing patterns, understanding language, or making predictions.
Machine Learning (Flipping the Approach): Traditional programming means writing explicit, deterministic rules yourself. Machine learning flips this around: you feed the system data and examples, and the machine learns the rules directly from the data itself.
Neural Networks & Deep Learning: Stacking layers of simple, connected units (input, hidden, and output layers) to adjust internal settings over and over until the network's output matches reality.
Transformers (The Big Breakthrough): The specific architecture built around a trick called attention. Instead of getting confused by long sentences, a transformer checks how relevant every single word is to every other word in a sentence all at once.
LLMs (Large Language Models): Sheer scale versions of the transformer architecture, containing billions of internal settings trained on massive pools of text from the internet.
Generative AI (A Broad Category): Older AI systems were built mostly to classify (e.g., look at a photo and say "that's a dog"). Generative AI does something fundamentally different: it creates brand new content (text, images, audio, or working code). An LLM is just one member of this category.
AI Agents (The Execution Loop): What happens when you wrap a model with real tools and a continuous loop. It moves the system from merely generating a response to actively pursuing a multi-step goal (perceiving data, planning steps, acting with tools, and adapting if things break).

Two Mental Distinctions That Clear Up the Noise

If you are trying to map your traditional engineering brain to these workflows, keep these two foundational concepts separate:

Training vs. Inference: Training is the massively expensive, heavy compute phase where data is ingested and the model learns its parameters. This happens once, long before a user touches it. Inference is the step that happens every single time you use the model—your prompt goes in, and the already-trained model processes it to predict tokens step-by-step.
Chatbot vs. Agent: A chatbot might give you a tidy, static list of suggestions for a trip. An agent will actively parse your calendar, run real flight searches, look up hotel API availability, and execute concrete steps toward a goal entirely on its own.

Let's discuss: For anyone else who transitioned from traditional software engineering into building or integrating with these systems, what specific concept took you the longest to wrap your head around?

Why AI Projects Are Never "Done": Speed running SDLC vs. ADLC

AICamp — Tue, 06 Oct 2026 22:35:12 +0000

Have you ever actually "finished" a traditional software engineering project? You write requirements, design it, build it, test it against a spec, deploy it, and maintain uptime. It's deterministic—same input, same output. "Done" is a real, achievable state.

The AI Development Lifecycle (ADLC) turns this upside down. It's probabilistic, it features 7 distinct phases instead of 5, and it operates as a continuous loop.

I put together a quick, 4-minute visual whiteboard breakdown speedrunning these phases and explaining why every stage of the lifecycle had to be reinvented, not just renamed:

The Reality of Shifting to ADLC:

Data Prep Eats Your Budget: Phase 2 (Data Collection and Preparation) quietly consumes roughly 80% of total project effort. Cleaning, labeling, and structuring data multiplies your worst legacy migration script by ten.
Pass/Fail vs. Ethical Trade-offs: In standard testing, a test passes or fails. In ADLC evaluation, you are stress-testing for precision, recall, bias, and adversarial robustness. For a cancer screening model, you must favor recall over precision—that's an ethical engineering trade-off, not a checklist.
Shadow Deployments: While blue/green rollbacks still exist, AI teams rely heavily on shadow deployments—running a new model silently alongside production traffic before letting it make autonomous decisions.
The Missing Phase: ADLC introduces a layer traditional software engineering never had to account for: continuous Governance and Ethics councils checking compliance across every single loop.

As engineers, our jobs aren't disappearing under this shift; they are moving from executing a static plan to overseeing a probabilistic system.

Let's discuss: How is your team balancing traditional software engineering infrastructure with these shifting AI deployment cycles? Are you using shadow deployments or sticking to rigorous local evaluation pipelines?

DEV Community: AICamp

The Layered Mental Model I Use to Understand Modern AI (As a Software Engineer)

The 7 Layers of the AI Stack

Two Mental Distinctions That Clear Up the Noise

Why AI Projects Are Never "Done": Speed running SDLC vs. ADLC

The Reality of Shifting to ADLC: