How AI's Actually Work — HAAW

Training Data

What the Model Learns From and What “Learning” Really Means — Training data supplies examples. Repeated forward passes, losses, gradients and optimizer updates leave behind a changed numerical model.

We Know How a Weight Changes

Forward pass. Loss. Backpropagation. Gradient. Optimizer. Updated weight. Now we ask what causes those changes, and what remains after enormous numbers of them.

TRAINING DATA ↓ tokens / examples ↓ forward pass → loss → backpropagation → gradients → optimizer ↓ CHANGED WEIGHTS

What Counts as Training Data?

Natural Language

Prose, dialogue, explanations, articles, books and other linguistic material.

Code

Programming languages, comments, documentation and structured syntax.

Structured Material

Markup, tables, mathematical notation and other serialized representations.

Curated Examples

Instruction-response examples, demonstrations, preference-related data and other post-training material.

Exact sources, mixtures, filters and stages vary by model.

Raw Material Is Not Yet a Training Batch

Source material
↓
Collection / selection
↓
Cleaning / filtering / formatting
↓
Possible deduplication / quality processing
↓
Tokenization
↓
Sequences and batches
↓
Training system

Pipelines differ, and these choices can materially affect the model that results.

One Sequence Can Supply Many Targets

Our earlier “The sky is → blue” example was intentionally tiny. Causal language-model pretraining can learn from many positions in the same sequence.

sequence: The sky is blue today The → sky The sky → is The sky is → blue The sky is blue → today

A compact implementation view is a one-token shift:

INPUT: [The] [sky] [is] [blue] TARGET: [sky] [is] [blue] [today]
The text supplies both preceding context and next-token targets. Many eligible positions can contribute to the objective in parallel.

From Text to Training Loss

Human-readable material
↓
Token IDs
↓
Batched tensors
↓
Transformer forward pass
↓
Vocabulary logits at many positions
↓
Compare predictions with target tokens
↓
Loss

The exact masking and reduction rules vary, but the GPU is ultimately processing numerical tensors—not “reading a book” in the human sense.

The Dataset Is Not Poured Into the Weights

If a training passage says that shorter wavelengths of visible light scatter strongly in the atmosphere, normal parameter training does not create a neat internal record like this:

weight 812903 = "sky" weight 812904 = "blue" weight 812905 = "scattering"

Nor does it ordinarily copy each source document into a hidden filesystem inside the model.

An example contributes to numerical updates distributed across trainable parameters.

One Example, Then Another, Then Another

example / batch ↓ prediction losses ↓ gradient contributions ↓ optimizer update ↓ many slightly changed parameters repeat → repeat → repeat → ...

No single ordinary update needs to contain a recognizable concept. “Learning” is accumulated parameter change produced by the training process.

A Mechanical Definition of “Learning”

For HAAW: a model has learned from training when optimization has changed its parameters in ways that alter its behavior on inputs.

This definition does not require us to claim human-style awareness, intention or memory. It identifies what physically changed: numerical parameter tensors.

Generalization

If training only reproduced exact strings, an LLM would be little more than a giant lookup mechanism. Instead, useful models can apply learned statistical structure to inputs that are not exact training examples.

many examples of language, relationships and structure ↓ parameter optimization ↓ behavior on new inputs and new combinations

That transfer to previously unseen inputs is generalization. A model need not have encountered the exact sentence “The purple tractor waited beside the observatory” to process its linguistic relationships.

Memorization Is Real Too

Generalization

Learned behavior transfers beyond exact training examples.

Memorization

Enough example-specific information is retained for particular material to be reproduced or closely reflected.

Models can sometimes reproduce specific training material, especially when material is repeated, distinctive, or otherwise strongly learned.

Generalization and memorization are not mutually exclusive. A model can do both.

Memorization Is Still Not Necessarily a Stored Document

NOT NECESSARILY: weights → locate document #7182 → read paragraph → output CORE EXECUTION STILL LOOKS LIKE: weights + current context → transformer computation → logits → tokens

Highly specific behavior can be encoded in a parameter configuration without a conventional intact file being stored inside the weights.

Data Alone Does Not Determine the Result

DATA + OBJECTIVE + OPTIMIZATION ↓ TRAINED PARAMETERS

Frequency matters, but so do context, diversity, data mixture, architecture, objective, batch construction, optimizer and other training choices.

Pretraining Is Not the Whole Story

Large-scale pretraining
↓
Possible supervised / instruction-oriented training
↓
Possible preference, reinforcement or other post-training
↓
Evaluation / refinement
↓
Deployed model

Different stages can use different data and objectives while retaining the same recognizable machinery: forward computation, objective/loss, gradients and parameter updates.

Training Data Can Carry Problems Too

training mixture ├── useful patterns ├── errors ├── contradictions ├── biases └── noise ↓ optimization receives training signals, not a perfect truth oracle

Filtering, curation, weighting, evaluation and post-training can address some problems, but none makes a corpus automatically true or complete.

Training Is Not Database Ingestion

Database

Explicit records are stored for retrieval through keys, queries or indexes.

Trained Model

Learned numerical parameters participate in computation that maps current inputs toward outputs.

A deployed AI system can combine both: an LLM plus search indexes, databases, retrieval, files, tools or persistent memory. HAAW keeps those layers separate.

Where Did the Training Data Go?

during training: examples → tensors → computation → loss → gradients → updates after training: the changed parameter state remains

The original corpus may remain separately stored by its owners, but ordinary inference does not require that corpus to accompany the deployed model. What the model carries forward is principally its learned parameter configuration.

Then Comes the Interesting Question

If the documents are not sitting inside the deployed model, how can it answer questions about things represented in them?

Because training changed the numerical function implemented by the model. A new prompt interacts with that learned parameter configuration, producing activations, logits and generated tokens.

past training examples ↓ influence WEIGHTS ↕ CURRENT CONTEXT ↓ activations → logits → generated tokens

“Knowledge” With a Warning Label

It is convenient to say a model “knows” something when its learned parameters support reliable behavior about that subject. HAAW keeps the mechanism underneath the shorthand visible:

not a librarian opening a fact card but learned parameters + current context + computation

This engineering description does not by itself settle philosophical questions about what should count as knowledge.

The Physical Journey

Stored training material
↓
Data pipeline / CPU / RAM
↓
Token tensors
↓
Accelerator memory
↓
Forward tensor operations
↓
Loss + backward operations
↓
Optimizer changes parameter tensors
↓
Updated checkpoint is saved

We have now traced “learning” from source material all the way to changed numerical state stored as a model.

What “Learning” Really Means

Training data supplies examples and training signals.

The model processes numerical representations of those examples.

The objective/loss measures performance; backpropagation computes gradients; the optimizer changes trainable parameters.

Repeated updates can produce generalization while also allowing some memorization.

When training ends, the central artifact is not a stack of readable source documents hidden inside the GPU. It is a trained numerical model.

HAAW Checkpoint

Training data
Examples or signals used to drive parameter updates during training stages.
Data pipeline
Processing that selects, cleans, formats, tokenizes, batches and delivers examples.
Next-token objective
Using preceding tokens to predict subsequent tokens.
Shifted targets
Offset input/target sequences allowing many positions to contribute prediction losses.
Generalization
Useful learned behavior on inputs that are not exact copies of training examples.
Memorization
Retention of enough example-specific information to reproduce or closely reflect particular material.
Pretraining
Large-scale initial training that establishes broad model capabilities.
Post-training
Additional training stages that modify a pretrained model toward further objectives.
Training versus storage
Training changes model parameters; it is not equivalent to inserting documents into a conventional database.
Central lesson
Training data shapes the final numerical parameter configuration through repeated loss, gradient and optimizer steps.