Neural Networks & Deep Learning: The Brain-Inspired Code

I spend roughly ₹60,000 a year on AI tools such as Claude, Gemini, GPT, and for a while, I assumed that investment would make my life frictionless. The reality was messier. I’d find myself teaching the same context to these models over and over, like a forgetful intern who keeps showing up without their notebook.

That déjà vu reminded me of something from a few years ago when I was managing my business Back then, I thought delegation meant giving instructions once and moving on. I built my team like how software is built: train, deploy, forget. And of course, things broke.

It wasn’t my team’s fault. I hadn’t created the decision models they needed. Eventually, I designed training modules that mapped what to do in different situations, when to decide, when to escalate, when something truly needed my attention.

Once those were in place, ninety percent of client issues resolved themselves. What changed wasn’t the people; it was the context.

AI doesn’t have that luxury. It wakes up blank every time you talk to it. Each prompt is a clean slate. Inside that emptiness are billions of tiny decision-makers—digital “neurons” casting weighted votes.

Some votes count more than others. If the total crosses a certain threshold, the system “fires” and the signal moves forward. It’s strangely democratic, except every member has a different number of ballots.

Stack enough of these neurons together and you get a neural network. Imagine thousands of boardrooms layered like floors in a skyscraper. The lower ones handle simple patterns, edges, pixels, basic words. Higher up, the network starts recognizing relationships: shapes, meanings, emotional tones. No single neuron “understands” the picture, but the collaboration produces something like comprehension.

Learning, for these systems, happens through backpropagation, the algorithm that powers the training phase. During training, the model makes a prediction, proceeds ahead , compares it to the correct answer, and then goes backward i.e. backpropagation, to calculate the error. It updates the internal “weights” so the next prediction is a little closer to correct. This cycle - forward, backward, adjust, repeats millions of times until the model becomes competent i.e. we achieve a error which is within tolerable range

But once the model is trained, that process stops. The weights freeze. What we use daily—ChatGPT, Claude, Gemini, operate only in inference mode. They don’t learn from new prompts; they apply the patterns already encoded in those frozen weights. In other words, there’s no backpropagation when you talk to them. That’s why your context matters so much.

When I work with AI now, I don’t fine-tune it. Fine-tuning sounds elegant but it’s expensive, slow, and model-dependent. Every time a new version launches, you’d have to retrain from scratch.

Instead, I’ve learned to engineer context—detailed, living documents that tell the model how I think, what my brand sounds like, and how I handle exceptions. It’s the same principle as training people, except you feed the context every time instead of assuming it remembers.

That’s the real work of modern AI adoption: not teaching, but reminding. We build systems that don’t “learn” in real time but adapt based on the scaffolding we provide. Context is how we simulate continuity in a stateless world.

Deep learning made that possible by building flexible pattern-recognition systems that could represent meaning without fixed rules. Geoffrey Hinton called these internal structures “flexible Lego blocks” i.e. building pieces that morph to capture relationships in data. When trained on millions of examples, those blocks form rich internal representations that help AI see structure in chaos.

That’s why today’s models can recognize a car in a blurry photo or understand the tone of an email. They don’t follow step-by-step logic; they recognize patterns of resemblance. The same way you recognize a friend’s handwriting even when they switch pens.

So, when I provide an AI with a detailed context doc i.e. a set of my rules, tone, and preference, I’m not teaching it new math. I’m giving it a lens through which to interpret patterns it already knows. Context becomes my version of backpropagation: not a mathematical update, but a narrative one.

That’s how AI fits into real workflows. It’s not learning from you in the moment; it’s aligning to you. And that distinction changes everything.

In human terms, learning means adjusting understanding. In AI terms, learning stops after training, and inference begins. The bridge between the two is context engineering—our way of injecting temporary memory into a system that otherwise forgets everything.

So here’s the principle I’ve come to live by: train people to remember, and train AI to re-learn. Humans accumulate context; AI reconstructs it every time. The better you design those scaffolds—the prompts, documents, examples—the more reliable your outcomes become.

Because intelligence, whether human or artificial, isn’t about remembering everything. It’s about learning faster than you fail.

Context Engineering: The New Backpropagation for Humans

Backpropagation taught machines how to learn from mistakes. Context engineering teaches humans how to make machines useful.

Once a model is trained, its learning stops. The weights freeze; the intelligence is static. But the world it operates in keeps changing: new rules, new risks, new interpretations. That’s where we come in. Context engineering is our version of continuous learning. It’s how we feed living context into static systems so they behave intelligently inside a specific environment.

Think of it like this: traditional AI training shapes the engine, but context engineering ensures that engine fits the right vehicle, runs on the right terrain, and understands the driver’s intent. A brilliant model without context is just horsepower with no steering.

This is why new roles are emerging — Context Engineers who bridge technical logic and business reality, and Domain Experts who provide the deep, sector-specific understanding that no dataset can encode. Their shared mission is simple: keep AI grounded in the real world.

In practice, context engineering means continuously defining the environment where AI operates i.e the mission, the human values, the risks, the constraints. It means testing and validating systems not in isolation, but in their intended use. It means designing interpretability so that “what,” “how,” and “why” all make sense together. And it means listening, building feedback loops that capture how systems behave in the wild and adjusting the context accordingly.

If training is the science of precision, context engineering is the art of fit. It’s what turns a large language model into a trustworthy assistant, or a generic classifier into a dependable business tool.

So, while backpropagation happens inside the model, context engineering happens around it. One tunes the math; the other tunes the meaning. Together, they close the loop between algorithmic intelligence and human understanding.