Introduction.

What if every stage of your life mapped precisely onto one of the three LLM architectures? Here's how I lived through each one.

I've spent years studying how AI systems learn, represent knowledge, and generate outputs. But it wasn't until I sat back and looked at my own life that something clicked. I've been living through these architectures all along.

There are exactly three types of LLM architecture. And they map almost perfectly onto three phases of a knowledge worker's career.

Life is a model in training. Each stage builds the foundation for the next.

Phase 1: School & College: The Encoder

Encoder-only phase

AI Architecture: Encoder-only (BERT, RoBERTa) · Focus: Absorb & Represent

From school through college, I was in pure encoder mode. In school I absorbed raw facts; in college I connected them across domains and built deeper internal representations. Both stages share the same architectural principle take input and build a rich embedding. No generation required yet.

• Learned facts & concepts
• Connected ideas across domains
• Understood language & context
• Applied theory to practice
• Classified good vs bad
• Built knowledge embeddings

An encoder-only model like BERT takes raw text and transforms it into rich, dense vector representations. It doesn't generate anything its entire purpose is to build the best possible internal model of the input. BERT is extraordinarily good at understanding; it just can't write back to you.

That's exactly what school and college do. You're not expected to ship products in year one of university. You're building the model that will let you do that later.

The AI parallel: BERT-style encoders produce embeddings that downstream tasks (classification, search, NLI) rely on. They're the foundation. College graduates are the same not yet specialized for generation, but deeply capable of understanding. The depth of that encoding determines everything that follows.

Phase 2: Industry: The Decoder

Decoder-only phase

AI Architecture: Decoder-only (GPT-4, Llama, Mistral) · Focus: Generate & Produce

When I entered the workforce, the mode shifted completely. Now I had to deliver. Write the code. Solve the problem. Ship the product. I was drawing on everything I had encoded to generate real outputs in the world.

• Created & developed applications
• Solved customer problems
• Answered queries & provided solutions
• Wrote code & documentation
• Optimized & improved systems
• Delivered business value

Decoder-only models like GPT take a context (prompt) and generate token by token from their learned knowledge. They don't need to re-encode everything from scratch they draw on rich internal representations built during training. That's exactly what a working engineer does: your years of encoding are now the weights. You generate from them.

The danger here? Pure decoders can hallucinate. They generate fluently even when uncertain. I made that mistake early in my career — confident outputs that needed more grounding in the actual requirements.

Phase 3 : AI Solution Architect: The Encoder–Decoder

Encoder–Decoder phase
AI Architecture: Encoder–Decoder (T5, BART, original Transformer) · Focus: Translate & Architect

As a Solution Architect, I do both at once. I encode the business requirements, constraints, team dynamics, stakeholder context. Then I decode into technical reality system design, roadmaps, team guidance. I'm the bridge between two languages.

• Encode stakeholder needs & context
• Understand BRD & business requirements
• Design system architecture
• Translate to developers
• Guide team & solve complex problems
• Deliver end-to-end solutions

The original Transformer encoder–decoder designed for translation is architecturally brilliant because of cross-attention. The decoder doesn't ignore the encoder's output while generating; it continuously attends to it. Every token generated is informed by the full encoded context.

That is solution architecture. You never stop listening to the business while designing the technical solution. The moment you decouple from the encoder (the business context), you start generating hallucinations technically correct solutions that solve the wrong problem.

The sharpest insight: Cross attention is the skill that separates architects from pure engineers. A decoder-only engineer generates great code. An encoder–decoder architect generates great code that solves the actual business problem because they never stopped attending to the encoded context.

Here’s a fact-checked and refined version that aligns more accurately with how Transformer architectures actually work while preserving your analogy and narrative style:

Why This Matters

Most people get trapped in a single architecture.

Some remain in an Encoder-only phase for years constantly learning, collecting certifications, reading books, attending courses, and building deeper internal understanding, but rarely translating that knowledge into real world outcomes.

In AI terms, encoder models like BERT specialize in understanding, contextual representation, classification, and semantic relationships. They are exceptional at comprehension, but they are not primarily designed for generation.

Other professionals operate like Decoder-only systems always producing output, writing code, creating presentations, answering questions, or generating solutions rapidly, but without deeply understanding the underlying problem space or business context first.

Decoder only LLMs such as GPT models are extremely powerful generators, but because they predict the next token based on patterns rather than grounded understanding alone, they can sometimes hallucinate when context, retrieval, or reasoning is insufficient.

The same pattern appears in professional life.

People who generate without deeply encoding the problem space often create shallow solutions, misaligned architectures, or confident but weak decisions.

The real evolution is becoming an Encoder–Decoder system.

Modern encoder–decoder architectures l*ike T5 and BART first encode context into rich internal representations and then decode that understanding into meaningful outputs.* The decoder continuously attends to the encoded context through mechanisms such as cross-attention.

That is what mature professionals eventually become.

A strong Solution Architect, engineering leader, researcher, or consultant operates like an encoder–decoder system.

• Encoding stakeholder intent, constraints, business goals, and domain context
• Decoding that understanding into technical systems, architecture, applications, and delivery plans
• Continuously connecting understanding and generation through feedback loops

That “cross-attention” between understanding and execution is where real impact happens.

It enables people to:

• Translate ambiguity into architecture
• Connect business and technology
• Generate solutions grounded in context
• Balance theory with execution
• Lead systems rather than simply produce output

Learning alone is not enough.
Generation alone is not enough.

Growth happens when understanding and creation operate together.

Just as AI evolved from isolated encoder or decoder models into full Transformer systems capable of both understanding and generation, human professional growth follows a similar path.

Key Takeaway

There are only 3 LLM architectures. There are only 3 phases of a knowledge career. They are the same thing expressed in different domains.

The best engineers, leaders, and architects run encoder–decoder with full cross-attention. They never stop encoding the context while generating the solution.

Learn → Create → Architect → Impact

Thanks
Sreeni Ramadorai