GPT and BERT Architecture: Encoder, Decoder, Pretraining, and Transformer-Based Language Models¶

A practical, engineering-focused guide to GPT and BERT architecture, explaining Transformer encoders and decoders, bidirectional versus autoregressive language modeling, tokenization, embeddings, self-attention, pretraining objectives, fine-tuning, inference, architectural differences, and their evolution into modern Large Language Models (LLMs).

1. Overview¶

GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) are two of the most influential Transformer-based language model architectures.

Both are built on the Transformer architecture, but they use different parts of the Transformer and are optimized for different objectives.

The fundamental distinction is:

BERT
 ↓
Encoder-Based Transformer
 ↓
Bidirectional Context
 ↓
Language Understanding

GPT
 ↓
Decoder-Based Transformer
 ↓
Autoregressive Context
 ↓
Language Generation

This distinction is important because it explains why:

• BERT became highly influential for language understanding tasks.
• GPT became the foundation for modern generative LLMs.

2. GPT vs BERT at a Glance¶

3. Transformer Architecture Foundation¶

Both GPT and BERT originate from the Transformer architecture introduced in:

Attention Is All You Need

The original Transformer consists of:

Encoder Stack
      +
Decoder Stack

Conceptually:

flowchart LR
    A["Input Sequence"] --> B["Encoder Stack"]
    B --> C["Contextual Representation"]
    C --> D["Decoder Stack"]
    D --> E["Output Sequence"]

BERT primarily uses:

Encoder Stack

GPT primarily uses:

Decoder Stack

This difference creates their different behaviors.

4. Transformer Building Blocks¶

Both architectures are constructed from variations of the same fundamental components:

Tokenization
     ↓
Token Embeddings
     +
Positional Information
     ↓
Attention
     ↓
Feed-Forward Network
     ↓
Normalization
     ↓
Residual Connections
     ↓
Repeated Transformer Layers

A simplified Transformer block is:

flowchart TD
    A["Input Representation"]
    B["Multi-Head Attention"]
    C["Add + Layer Normalization"]
    D["Feed-Forward Network"]
    E["Add + Layer Normalization"]
    F["Output Representation"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

The exact implementation differs between BERT and GPT, especially around attention masking and normalization placement.

5. Tokenization¶

Neither GPT nor BERT directly processes raw text.

The input text is first converted into tokens.

flowchart LR
    A["Raw Text"] --> B["Tokenizer"]
    B --> C["Tokens"]
    C --> D["Token IDs"]
    D --> E["Embedding Layer"]

For example:

"AI engineering is powerful"

        ↓

["AI", "engineering", "is", "powerful"]

        ↓

[101, 2345, 2003, 3928]

Actual tokenization and token IDs depend on the model's tokenizer.

6. Input Embeddings¶

Token IDs are mapped to dense vectors.

Conceptually:

Token ID
   ↓
Embedding Matrix
   ↓
Dense Vector

For a sequence:

Token 1 → Vector 1
Token 2 → Vector 2
Token 3 → Vector 3
Token 4 → Vector 4

These vectors form the initial representation supplied to Transformer layers.

7. Positional Information¶

Transformers process tokens in parallel, so the model needs information about token positions.

Conceptually:

Token Embedding
      +
Position Information
      ↓
Transformer Input

For BERT, positional embeddings are traditionally added to token and segment embeddings.

For GPT-style models, positional information has evolved across model generations and may use approaches such as:

• Learned positional embeddings
• Rotary Position Embeddings (RoPE)
• Other relative or position-aware mechanisms

The detailed attention and positional encoding concepts are covered in:

05. Attention and Positional Encoding

8. BERT Architecture¶

BERT is an encoder-only Transformer architecture.

A simplified architecture is:

flowchart TD
    A["Input Text"]
    B["Tokenizer"]
    C["Token + Segment + Position Embeddings"]
    D["Transformer Encoder Layer"]
    E["Transformer Encoder Layer"]
    F["..."]
    G["Final Contextual Representations"]
    H["Task-Specific Head"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H

The core idea is that every token can attend to relevant tokens on both sides of the sequence.

9. BERT Bidirectional Attention¶

Consider:

The bank approved the loan.

BERT can use context from both directions when building the representation of bank.

Conceptually:

The  ←──┐
        │
bank ←──┼──→ approved
        │
loan ←──┘

More generally:

Previous Tokens
       ↘
        Current Token
       ↗
Following Tokens

This bidirectional context is one of BERT's defining characteristics.

10. BERT Self-Attention¶

BERT uses self-attention to allow every token to interact with other tokens in the sequence.

For:

The customer opened an account

attention can model relationships such as:

customer ↔ opened
customer ↔ account
opened   ↔ account

Conceptually:

flowchart TD
    A["The"]
    B["customer"]
    C["opened"]
    D["an"]
    E["account"]

    A <--> B
    B <--> C
    B <--> E
    C <--> E
    D <--> E

The actual attention mechanism assigns learned weights rather than simply treating every relationship equally.

11. BERT Encoder Layer¶

A simplified BERT encoder layer is:

Input
  ↓
Multi-Head Self-Attention
  ↓
Residual Connection
  ↓
Layer Normalization
  ↓
Feed-Forward Network
  ↓
Residual Connection
  ↓
Layer Normalization
  ↓
Output

Mermaid representation:

flowchart TD
    A["Input"]
    B["Multi-Head Self-Attention"]
    C["Residual + LayerNorm"]
    D["Feed-Forward Network"]
    E["Residual + LayerNorm"]
    F["Output"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

Multiple encoder layers are stacked to create the complete BERT model.

12. BERT Input Representation¶

Original BERT combines multiple types of embeddings.

Token Embedding
       +
Segment Embedding
       +
Position Embedding
       ↓
Transformer Encoder

Conceptually:

flowchart TD
    A["Token Embedding"]
    B["Segment Embedding"]
    C["Position Embedding"]
    D["Combined Input Representation"]

    A --> D
    B --> D
    C --> D

    D --> E["BERT Encoder"]

Segment embeddings were especially useful for tasks involving sentence pairs.

13. Special Tokens in BERT¶

BERT uses special tokens for different purposes.

Important examples include:

[CLS]
[SEP]
[MASK]
[PAD]

[CLS]¶

Represents the beginning of the sequence and is commonly used as an aggregate representation for classification tasks.

[SEP]¶

Separates sentences or marks the end of a segment.

[MASK]¶

Used during Masked Language Model pretraining.

[PAD]¶

Used to make sequences within a batch the same length.

Example:

[CLS] The bank approved the loan [SEP]

14. BERT Pretraining¶

The original BERT training approach used two major objectives:

1. Masked Language Modeling
2. Next Sentence Prediction

15. Masked Language Modeling¶

In Masked Language Modeling (MLM), some input tokens are hidden and the model learns to predict them.

Example:

The customer opened a [MASK].

Target:

The customer opened a bank account.

Conceptually:

flowchart LR
    A["Input with Masked Token"] --> B["BERT Encoder"]
    B --> C["Contextual Representation"]
    C --> D["Prediction Head"]
    D --> E["Masked Token"]

The key advantage is that the model can use both left and right context.

16. Why Masked Language Modeling Works¶

Consider:

The customer deposited money into the [MASK].

The model can use:

Left Context:
The customer deposited money into the

+

Right Context:
possibly surrounding words

↓

Predict:
bank

This encourages BERT to learn contextual representations rather than simply learning a left-to-right generation process.

17. Next Sentence Prediction¶

Original BERT also introduced Next Sentence Prediction (NSP).

The model receives two sentence segments:

Sentence A
+
Sentence B

and predicts whether B logically follows A.

Example:

A:
The customer opened a new account.

B:
The bank issued a debit card.

The model attempts to classify the relationship.

NSP was intended to help BERT learn relationships between sentences.

Later research showed that NSP is not always necessary, and several BERT-family models modified or removed this objective.

18. GPT Architecture¶

GPT uses a decoder-only Transformer architecture.

A simplified architecture is:

flowchart TD
    A["Input Text"]
    B["Tokenizer"]
    C["Token Embeddings + Position Information"]
    D["Masked Self-Attention"]
    E["Feed-Forward Network"]
    F["Transformer Decoder Block"]
    G["Repeated Decoder Blocks"]
    H["Vocabulary Projection"]
    I["Next Token Probabilities"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I

Unlike the original Transformer decoder, GPT-style models generally use decoder blocks without the encoder-decoder cross-attention layer because they operate as decoder-only models.

19. Causal Self-Attention¶

The defining property of GPT is causal attention.

A token can attend only to previous tokens and itself.

For:

The customer opened an account

the attention pattern is approximately:

Token 1 → Token 1

Token 2 → Token 1, Token 2

Token 3 → Token 1, Token 2, Token 3

Token 4 → Token 1, Token 2, Token 3, Token 4

Token 5 → Token 1, Token 2, Token 3, Token 4, Token 5

Conceptually:

        Token 1  Token 2  Token 3  Token 4
Token 1    ✓        ✗        ✗        ✗
Token 2    ✓        ✓        ✗        ✗
Token 3    ✓        ✓        ✓        ✗
Token 4    ✓        ✓        ✓        ✓

This prevents the model from seeing future tokens during training.

20. Causal Attention Mask¶

The attention mask can be represented mathematically as a lower-triangular matrix.

┌───┬───┬───┬───┐
│ ✓ │ ✗ │ ✗ │ ✗ │
├───┼───┼───┼───┤
│ ✓ │ ✓ │ ✗ │ ✗ │
├───┼───┼───┼───┤
│ ✓ │ ✓ │ ✓ │ ✗ │
├───┼───┼───┼───┤
│ ✓ │ ✓ │ ✓ │ ✓ │
└───┴───┴───┴───┘

This is what enables autoregressive generation.

21. GPT Language Modeling Objective¶

GPT is trained using next-token prediction.

Given:

The customer opened

the model predicts:

an

Then:

The customer opened an

predicts:

account

Conceptually:

flowchart LR
    A["Context Tokens"] --> B["GPT"]
    B --> C["Next Token Distribution"]
    C --> D["Selected Token"]
    D --> E["Extended Context"]
    E --> B

The process is repeated during generation.

22. Autoregressive Generation¶

During inference, GPT generates tokens one at a time.

Example:

Prompt:

The future of AI is

        ↓

The future of AI is intelligent

        ↓

The future of AI is intelligent automation

        ↓

The future of AI is intelligent automation systems

Conceptually:

flowchart TD
    A["Prompt"]
    B["Predict Next Token"]
    C["Append Token"]
    D["Check Stop Condition"]

    A --> B
    B --> C
    C --> D
    D -->|Continue| B
    D -->|Stop| E["Generated Sequence"]

This autoregressive process is fundamental to GPT-style LLMs.

23. BERT vs GPT Attention¶

The biggest attention difference can be visualized as:

BERT¶

Token
 ↙ ↓ ↘
Previous + Current + Future

GPT¶

Previous Tokens
       ↓
     Token

Therefore:

BERT → Bidirectional Attention
GPT  → Causal Attention

24. BERT vs GPT Training Objective¶

The training objectives are fundamentally different.

BERT¶

Masked Input
      ↓
Predict Missing Token

GPT¶

Previous Tokens
      ↓
Predict Next Token

Comparison:

BERT:

The customer opened a [MASK].

                    ↓
                  "bank"


GPT:

The customer opened a

                    ↓
                  "new"

25. BERT Architecture Diagram¶

flowchart TD
    A["Text"]
    B["Tokenizer"]
    C["Token + Segment + Position Embeddings"]
    D["Encoder Layer 1"]
    E["Encoder Layer 2"]
    F["..."]
    G["Encoder Layer N"]
    H["Contextual Representations"]
    I["Task Head"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I

BERT is therefore:

Input
 ↓
Encoder Stack
 ↓
Contextual Representation
 ↓
Task-Specific Head

26. GPT Architecture Diagram¶

flowchart TD
    A["Text"]
    B["Tokenizer"]
    C["Token + Position Embeddings"]
    D["Decoder Block 1"]
    E["Decoder Block 2"]
    F["..."]
    G["Decoder Block N"]
    H["Vocabulary Projection"]
    I["Next Token Probabilities"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> I

GPT is therefore:

Input
 ↓
Decoder Stack
 ↓
Vocabulary Distribution
 ↓
Next Token

27. BERT Task-Specific Heads¶

BERT's contextual representation can be connected to different task-specific heads.

flowchart TD
    A["BERT Encoder"]
    B["Contextual Representation"]

    B --> C["Classification Head"]
    B --> D["Token Classification Head"]
    B --> E["Question Answering Head"]
    B --> F["Similarity / Embedding Head"]

    C --> G["Class"]
    D --> H["Token Labels"]
    E --> I["Answer Span"]
    F --> J["Similarity Score"]

Common applications include:

• Sentiment Analysis
• Document Classification
• Intent Classification
• Named Entity Recognition
• Extractive Question Answering
• Semantic Similarity

28. GPT Application Pattern¶

GPT's autoregressive architecture naturally supports generation.

flowchart TD
    A["Prompt"]
    B["GPT"]
    C["Generated Tokens"]
    D["Application"]

    A --> B
    B --> C
    C --> B
    C --> D

Applications include:

• Chatbots
• Text Completion
• Summarization
• Code Generation
• Question Answering
• Content Generation
• AI Assistants

29. BERT and GPT: Different Design Goals¶

A useful way to remember the difference is:

BERT asks:

"What does this text mean?"

GPT asks:

"What token should come next?"

This is a simplification, but it captures their original architectural intent.

30. Encoder-Only vs Decoder-Only¶

The distinction can be generalized.

Encoder-Only¶

Input
 ↓
Encoder
 ↓
Representation
 ↓
Task

Typical use:

• Understanding
• Classification
• Retrieval
• Representation Learning

Examples:

• BERT
• RoBERTa
• DistilBERT

Decoder-Only¶

Input
 ↓
Decoder
 ↓
Next Token
 ↓
Generated Sequence

Typical use:

• Text Generation
• Code Generation
• Conversational AI
• General-purpose LLMs

Examples:

• GPT
• Llama
• Mistral
• Qwen

31. Encoder-Decoder Models¶

There is a third important architecture family:

Encoder-Decoder Transformers

flowchart LR
    A["Input Sequence"] --> B["Encoder"]
    B --> C["Context Representation"]
    C --> D["Decoder"]
    D --> E["Output Sequence"]

Examples include:

• T5
• BART

These architectures are particularly useful for sequence-to-sequence tasks.

Examples:

• Translation
• Summarization
• Text Transformation

Therefore, modern Transformer model families can be broadly categorized as:

Transformer Models
       │
       ├── Encoder-Only
       │      └── BERT
       │
       ├── Decoder-Only
       │      └── GPT
       │
       └── Encoder-Decoder
              └── T5 / BART

32. Pretraining vs Fine-Tuning¶

Both BERT and GPT introduced the idea of large-scale pretraining followed by adaptation.

The general workflow is:

flowchart TD
    A["Large-Scale Unlabeled Data"]
    B["Pretraining"]
    C["Pretrained Model"]
    D["Task-Specific Dataset"]
    E["Fine-Tuning"]
    F["Downstream Application"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

This approach dramatically reduced the amount of task-specific training required.

33. BERT Fine-Tuning¶

A BERT model can be fine-tuned for a classification problem.

Pretrained BERT
      ↓
Customer Support Dataset
      ↓
Fine-Tuning
      ↓
Intent Classifier

Example:

Input:
"I forgot my password."

        ↓

BERT

        ↓

PASSWORD_RESET

34. GPT Adaptation¶

GPT-style models can also be adapted through multiple approaches.

Pretrained GPT
      ↓
Prompting
      ↓
Instruction Tuning
      ↓
Fine-Tuning
      ↓
Preference Optimization

Modern LLM development extends far beyond the original GPT training paradigm.

Later chapters cover:

• Supervised Fine-Tuning
• PEFT
• LoRA
• QLoRA
• Instruction Tuning
• Reward Modeling
• RLHF
• PPO
• DPO

35. Parameter Sharing and Scaling¶

Transformer language models scale through increasing:

• Number of layers
• Hidden dimensions
• Attention heads
• Vocabulary size
• Training data
• Parameter count

A simplified relationship is:

More Data
    +
More Parameters
    +
More Compute
    ↓
Larger Pretrained Model

However, scaling is not simply about increasing model size.

Modern model engineering also considers:

• Data quality
• Training efficiency
• Architecture
• Inference efficiency
• Context length
• Alignment
• Evaluation

36. Contextual Representations¶

One of BERT's major contributions was demonstrating the power of contextual representations.

Consider:

I deposited money at the bank.

and:

I sat beside the river bank.

The representation of bank should depend on context.

Conceptually:

bank + financial context
        ↓
Financial Representation

bank + river context
        ↓
Geographical Representation

This is significantly more powerful than assigning one static vector to every occurrence of a word.

37. GPT Context Modeling¶

GPT also creates contextual representations, but within a causal generation framework.

For:

The customer opened an

the representation used to predict the next token depends on the preceding context.

The
 ↓
customer
 ↓
opened
 ↓
an
 ↓
?

The model predicts:

P(next_token | previous_tokens)

Conceptually:

$$ P(x_t \mid x_1,x_2,\ldots,x_{t-1}) $$

This probability distribution drives autoregressive generation.

38. BERT Objective vs GPT Objective¶

The two objectives can be summarized mathematically.

BERT¶

BERT learns to predict masked tokens:

$$ P(x_i \mid x_{\setminus i}) $$

where the model uses surrounding context to predict the masked token.

GPT¶

GPT learns:

$$ P(x_t \mid x_1,\ldots,x_{t-1}) $$

where each token is predicted using preceding tokens.

This difference explains much of the architectural behavior of the two model families.

39. Attention Mask Comparison¶

A simplified comparison:

BERT

      1  2  3  4
1     ✓  ✓  ✓  ✓
2     ✓  ✓  ✓  ✓
3     ✓  ✓  ✓  ✓
4     ✓  ✓  ✓  ✓

Every token can attend to every other token.

GPT:

      1  2  3  4
1     ✓  ✗  ✗  ✗
2     ✓  ✓  ✗  ✗
3     ✓  ✓  ✓  ✗
4     ✓  ✓  ✓  ✓

Future tokens are masked.

40. Why GPT Became Dominant for Generative AI¶

GPT-style decoder-only architectures became highly influential because they combine:

• Simple autoregressive objective
• Large-scale pretraining
• Scalable Transformer architecture
• Natural text generation
• In-context learning
• Prompt-based interaction
• Strong transfer across tasks

The architectural pattern became:

Large Dataset
      ↓
Self-Supervised Pretraining
      ↓
Large Decoder-Only Transformer
      ↓
Instruction / Preference Adaptation
      ↓
General-Purpose LLM

This architecture is now central to modern Generative AI.

41. BERT's Continuing Importance¶

Although decoder-only LLMs dominate many generative workloads, encoder-based models remain valuable.

BERT-style models can still be effective for:

• Classification
• NER
• Semantic Search
• Embeddings
• Reranking
• Document Understanding
• Lightweight NLP inference

For some tasks, a smaller encoder model may be more efficient than using a large generative LLM.

This is an important production engineering consideration.

42. GPT vs BERT: Production Decision¶

A simplified decision framework:

flowchart TD
    A["NLP Requirement"]
    B{"Need Generation?"}
    C["Decoder-Only / GPT-Style"]
    D{"Need Representation / Classification?"}
    E["Encoder-Only / BERT-Style"]
    F["Consider Encoder-Decoder"]

    A --> B
    B -->|Yes| C
    B -->|No| D
    D -->|Yes| E
    D -->|No| F

Use cases should drive architecture selection.

Do not automatically select an LLM simply because it is the newest model.

43. Practical PyTorch Representation¶

A simplified BERT-style architecture can be represented as:

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")

inputs = {
    "input_ids": torch.tensor([[101, 2023, 2003, 102]]),
    "attention_mask": torch.tensor([[1, 1, 1, 1]])
}

outputs = model(**inputs)

hidden_states = outputs.last_hidden_state

print(hidden_states.shape)

The output provides contextual representations for the input tokens.

44. Practical GPT-Style Representation¶

A simplified GPT-style model can be represented using a causal language model:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "gpt2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

inputs = tokenizer(
    "The future of AI is",
    return_tensors="pt"
)

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits

print(logits.shape)

The final logits represent the model's predicted distribution over the vocabulary for each position.

45. GPT Text Generation¶

A simplified Hugging Face generation workflow is:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "gpt2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Artificial intelligence will"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50
)

text = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(text)

The important engineering flow is:

Prompt
 ↓
Tokenizer
 ↓
Token IDs
 ↓
Decoder-Only Transformer
 ↓
Logits
 ↓
Decoding Strategy
 ↓
Generated Tokens
 ↓
Text

46. BERT Classification Workflow¶

A simplified classification architecture is:

flowchart TD
    A["Text"]
    B["Tokenizer"]
    C["BERT"]
    D["CLS Representation"]
    E["Classification Head"]
    F["Class Probability"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

Example:

Customer:
"My card is not working."

        ↓

BERT

        ↓

CARD_PROBLEM

47. GPT Generation Workflow¶

A simplified GPT generation architecture is:

flowchart TD
    A["Prompt"]
    B["Tokenizer"]
    C["Token IDs"]
    D["GPT Transformer"]
    E["Logits"]
    F["Sampling / Decoding"]
    G["Next Token"]
    H["Updated Context"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> D

This loop continues until:

• Maximum token limit
• End-of-sequence token
• Application-defined stop condition

48. GPT and BERT in the Modern LLM Ecosystem¶

The evolution can be summarized as:

flowchart TD
    A["Transformer"]
    B["BERT"]
    C["GPT"]
    D["T5 / BART"]
    E["Modern Encoder Models"]
    F["Modern Decoder LLMs"]
    G["Multimodal Foundation Models"]

    A --> B
    A --> C
    A --> D
    B --> E
    C --> F
    D --> G
    F --> G

BERT and GPT therefore represent two important branches of the Transformer ecosystem.

49. Key Architectural Differences¶

50. BERT vs GPT Architecture Graph¶

                    TRANSFORMER
                        │
            ┌───────────┴───────────┐
            │                       │
         ENCODER                  DECODER
            │                       │
          BERT                    GPT
            │                       │
     Bidirectional              Causal
       Attention               Attention
            │                       │
   Understanding                  Generation
            │                       │
 Classification                Next Token
 NER / QA / Search            Chat / Code / Content

51. Common Misconceptions¶

Misconception 1: BERT and GPT Are Completely Different Architectures¶

They are not.

Both are based on the Transformer architecture.

The major difference is which Transformer component and attention strategy they use.

Misconception 2: GPT Cannot Understand Language¶

GPT learns rich contextual representations as part of next-token prediction.

Its architecture supports both understanding and generation, although its primary training objective is autoregressive generation.

Misconception 3: BERT Cannot Be Used in Generative Systems¶

BERT itself is not primarily designed for autoregressive generation, but BERT-style encoders can be components of larger systems.

Misconception 4: Every Transformer Is an LLM¶

A Transformer architecture can be used for many tasks.

Not every Transformer model is a large language model.

Misconception 5: Bigger Models Always Perform Better¶

Production performance depends on:

• Task
• Data
• Model architecture
• Evaluation
• Cost
• Latency
• Context
• Deployment environment

52. Production Considerations¶

When choosing between encoder and decoder architectures, evaluate:

Latency¶

Encoder-only models can be much cheaper for simple classification tasks.

Throughput¶

Task-specific models may provide higher throughput than general-purpose LLMs.

Cost¶

Using a large generative model for a simple classification task may be unnecessarily expensive.

Accuracy¶

Architecture should match the task.

Maintainability¶

Separate model inference from business logic.

Deployment¶

Consider:

• CPU inference
• GPU inference
• Batch processing
• Quantization
• Model serving
• Autoscaling

53. Interview Questions¶

Beginner¶

1. What is BERT?
2. What is GPT?
3. What does GPT stand for?
4. What does BERT stand for?
5. What is the Transformer architecture?
6. Encoder vs decoder?
7. What is self-attention?
8. What is masked language modeling?
9. What is next-token prediction?

Intermediate¶

1. Why is BERT bidirectional?
2. Why does GPT use causal attention?
3. What is the difference between MLM and autoregressive language modeling?
4. Why is BERT useful for classification?
5. Why is GPT useful for generation?
6. What are [CLS], [SEP], and [MASK]?
7. What is causal masking?
8. How does GPT generate text?
9. How does BERT create contextual representations?
10. Encoder-only vs decoder-only vs encoder-decoder?

Advanced¶

1. Why did decoder-only Transformers become dominant for modern LLMs?
2. Why might an encoder model be preferable to an LLM for classification?
3. How would you choose between BERT and GPT for an enterprise application?
4. What are the production trade-offs between encoder and decoder architectures?
5. How does causal masking affect training and inference?
6. Why can GPT perform multiple tasks without task-specific heads?
7. How does self-supervised pretraining enable transfer learning?
8. How would you optimize GPT inference for high throughput?
9. How would you deploy a BERT classifier in a microservice architecture?
10. How would you evaluate an encoder-based model against an LLM for the same enterprise task?
11. What role does model size play in Transformer performance?
12. How do modern LLM architectures differ from the original GPT architecture?

54. 🚀 Quick Revision Sheet¶

BERT¶

Text
 ↓
Tokenizer
 ↓
Embeddings
 ↓
Encoder Stack
 ↓
Bidirectional Attention
 ↓
Contextual Representation
 ↓
Task Head
 ↓
Prediction

GPT¶

Prompt
 ↓
Tokenizer
 ↓
Embeddings
 ↓
Decoder Stack
 ↓
Causal Attention
 ↓
Logits
 ↓
Next Token
 ↓
Generated Text

BERT Objective¶

Masked Token
     ↓
Predict Missing Token

GPT Objective¶

Previous Tokens
      ↓
Predict Next Token

Architecture¶

Transformer
   │
   ├── Encoder-Only
   │       └── BERT
   │
   ├── Decoder-Only
   │       └── GPT
   │
   └── Encoder-Decoder
           └── T5 / BART

55. Key Takeaways¶

• BERT and GPT are both based on the Transformer architecture.
• BERT is primarily an encoder-only Transformer.
• GPT is primarily a decoder-only Transformer.
• BERT uses bidirectional self-attention for contextual language understanding.
• GPT uses causal self-attention so that each token can only attend to previous tokens.
• BERT was originally pretrained using Masked Language Modeling and Next Sentence Prediction.
• GPT is pretrained using autoregressive next-token prediction.
• BERT is highly effective for classification, NER, semantic representation, and other language understanding tasks.
• GPT-style architectures are naturally suited to text generation and have become the dominant architecture for many modern LLMs.
• Encoder-decoder Transformers such as T5 provide another important architecture family for sequence-to-sequence tasks.
• Tokenization, embeddings, positional information, attention, feed-forward networks, residual connections, and normalization are core Transformer components.
• Pretraining allows models to learn general-purpose language representations from large datasets.
• Fine-tuning and other adaptation techniques transform pretrained models into specialized enterprise AI solutions.
• Model architecture should be selected based on the business problem, quality requirements, latency, cost, scalability, and deployment constraints rather than simply choosing the largest available model.
• Understanding BERT and GPT provides the architectural foundation for understanding modern Foundation Models, LLMs, fine-tuning, PEFT, instruction tuning, RLHF, DPO, and production Generative AI systems.

56. Chapter Navigation¶

Previous¶

05. Attention and Positional Encoding

Next¶

07. Hugging Face and Transformers

Related¶

03. Word Embeddings

04. Language Modeling

05. Attention and Positional Encoding

References¶

• Vaswani et al. — Attention Is All You Need
• Devlin et al. — BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Radford et al. — Improving Language Understanding by Generative Pre-Training
• Radford et al. — Language Models are Unsupervised Multitask Learners
• Brown et al. — Language Models are Few-Shot Learners
• Jurafsky & Martin — Speech and Language Processing
• Hugging Face Transformers Documentation
• PyTorch Documentation

Enterprise AI Engineering Handbook
Building Production-Grade Enterprise AI Systems — One Chapter at a Time.