What the basic components of AI are and how the data flows

[Chip (OP)]

What are the basic components of AI and show the data flow

The Conceptual AI stack

Tokenisation
↓
Neural Networks
↓
Transformers
↓
LLMs
↓
RAG


The real AI stack

Raw Data
↓
Tokenisation
↓
Embeddings
↓
Transformer Neural Network
↓
Pretraining
↓
LLM
↓
Alignment / Fine Tuning
↓
Inference Runtime
↓
Tools / APIs / Memory
↓
RAG / Agents / Long-Term Memory
↓
Applications


Core components of AI (neural network model)

1. Input data
Raw information such as:
- images, text, audio, sensor readings 
Converted into numeric form (vectors/tensors)

2. Parameters (weights + biases)
- Weights: control strength of connections 
- Biases: shift outputs 
These are the “learned memory” of the model.

3. Layers (transformations)
Each layer computes:

Code: [Select]

output = activation(Wx + b)
They progressively transform raw input into useful representations.

4. Activation function
Adds non-linearity:
- ReLU, sigmoid, tanh 
Without it, the model becomes just a linear function.

5. Loss function
Measures error:
- difference between prediction and truth 
Outputs a single number representing “how wrong” the model is.

6. Optimiser
Updates weights to reduce error:
- gradient descent / Adam 
It performs learning.

---

Data flow (forward + learning loop)

Forward pass

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Input
 ↓
Layer 1 (Wx + b + activation)
 ↓
Layer 2
 ↓
...
 ↓
Output (prediction)

Loss calculation

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Prediction + True label → Loss → Error value

Backward pass (backpropagation)

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Error
 ↓
Compute gradients (credit assignment)
 ↓
Propagate backwards through layers

Parameter update

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Weights = Weights - learning_rate × gradient

---

Full AI training loop

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Input → Forward pass → Prediction
                    ↓
                   Loss
                    ↓
            Backpropagation
                    ↓
           Update parameters
                    ↓
              Repeat many times

---

Key idea
AI is not rules or logic.
It is a feedback loop that tunes a large mathematical function until its outputs match data.

Generated by ChatGPT.

[smfadmin]

How A Query Moves/Flows Through AI And Becomes A Reply (According to ChatGPT)

A query like:

“Track this query as it moved through AI and when it becomes the reply”

passes through multiple transformation layers before you see text on screen.

Here’s the actual flow.


1. Raw User Input

You type characters into the client:

Code: [Select]

Track this query as it moved through AI and when it becomes the reply

At this point it is just UTF-8 text.

No meaning yet.
No intelligence yet.


2. Client Packaging

The app packages the message with metadata:

• conversation history
• timestamps
• system prompts
• tool availability
• memory context
• user settings
• safety policies

The model never receives “just your sentence”.

It receives a huge structured context window.

Conceptually:

Code: [Select]

{
  "system": "...",
  "developer": "...",
  "memory": "...",
  "conversation": [...],
  "new_user_message": "Track this query..."
}


3. Tokenisation

The text is split into tokens.

Not words.
Subword fragments.

Example approximation:

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["Track", " this", " query", " as", " it", " moved", ...]

Each token becomes an integer ID.

Example:

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[18374, 420, 9912, 328, ...]

This is the first point where language becomes machine-processable.


4. Embedding Projection

Each token ID is mapped into a high-dimensional vector.

Conceptually:

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"query" →
[-0.182, 0.991, 0.004, ...]

Now the sentence exists as geometry.

Meaning is represented as spatial relationships in vector space.

Words with similar meaning cluster nearby.


5. Positional Encoding

Transformers cannot inherently understand order.

So position information is injected:

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Token 1 + position encoding
Token 2 + position encoding
...

This lets the model distinguish:

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dog bites man

from:

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man bites dog


6. Transformer Attention Passes

This is the core intelligence layer.

The model repeatedly performs:

• self-attention
• weighting
• nonlinear transformations
• residual mixing
• layer normalization

Across dozens to hundreds of layers.

The sentence effectively becomes a dynamic graph of relationships.

The model starts constructing abstractions like:

• “user wants architecture explanation”
• “timeline/process requested”
• “technical depth preferred”
• “explanatory mode appropriate”

Attention dynamically links concepts:

Code: [Select]

"Track" ↔ process
"moved" ↔ pipeline
"reply" ↔ output generation


7. Internal Latent Reasoning

This part is invisible.

The model builds temporary internal representations:

• intent estimation
• response planning
• style selection
• safety evaluation
• tool routing possibilities
• probability forecasting

Not symbolic reasoning like code.

More like evolving activation fields.

There is no little narrator internally saying:

“I should explain transformers now.”

Instead:

billions of weighted activations statistically converge toward likely continuations.


8. Next-Token Prediction

The model does not generate replies all at once.

It predicts one token at a time.

Internally:

Code: [Select]

P("A") = 0.02
P("The") = 0.41
P("Your") = 0.03

The selected token becomes part of the context.

Then prediction repeats.

Loop:

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input → predict token → append token → predict next token

Thousands of times.

Your entire reply emerges autoregressively.


9. Decoding

Token IDs convert back into text.

Example:

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[32, 918, 4412]
↓
"The model predicts"

Now language reappears from vector space.


10. Postprocessing Layer

Before display:

• formatting is applied
• markdown rendered
• citations inserted
• safety filters checked
• UI elements added
• streaming chunked

11. Streaming To User

You see the response progressively appear.

But internally the model may already have generated much further ahead.

The stream is buffered and emitted incrementally.


12. When Does It “Become” The Reply?

Interesting philosophical point.

There are several candidates:

A. At tokenisation?
No — syntax only.

B. During latent representation?
Partially — semantic structure exists internally.

C. At first generated token?
Closer.

D. When decoding back into text?
Most practical definition.

E. When rendered to the user?
Human-visible completion.


The Deep Reality

The reply is not stored anywhere beforehand.

It is not retrieved from a database.

It does not exist until generated.

The model continuously collapses probability distributions into actual token choices in real time.

So the “reply” only truly comes into existence progressively during autoregressive decoding.


Extremely Simplified Stack

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User Text
   ↓
Tokenisation
   ↓
Vector Embeddings
   ↓
Transformer Attention
   ↓
Latent Semantic State
   ↓
Next-Token Prediction Loop
   ↓
Decoded Tokens
   ↓
Rendered Reply

Your earlier AI stack summary was actually fairly good:

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Tokenisation Pipeline
↓
Neural Networks
↓
Transformers
↓
LLMs
↓
RAG Systems

Though technically:

Transformers ARE a type of neural network, and LLMs are transformer-based systems trained at massive scale.

So structurally it’s closer to:

Code: [Select]

Tokenisation
↓
Embeddings
↓
Transformer Neural Network
↓
LLM Behaviour
↓
Tooling / Memory / RAG / Agents
↓
Final Response