Chats

Predictive vs Generative AI: How They Work and When to Use Each

IBM Technology

He makes mistakes I think… but good discussion.

just watched a video about predictive/generative ai. confusing.

in general, predictive AI is basically an AI assistant helping eAgent SW that actually runs the prediction algorithms?

Not exactly. The terminology gets overloaded. “Predictive AI” usually means:

AI predicts some future/unknown value from data.

Examples:

fraud risk
customer churn
equipment failure
stock movement probability
disease likelihood

In many classic predictive systems:

the neural network/model itself IS the prediction engine
there may not even be an LLM

Example:

sensor data
→ predictive model
→ probability of machine failure

That predictive model might be:

neural net
gradient boosting
regression
random forest
etc.

“Generative AI” usually means:

generate new outputs

Examples:

text
images
code
music
plans
JSON
dialogue

LLMs are generative AI systems.

Where your framework fits:

Modern agentic systems often combine:

predictive models
+
generative TF/LLM systems
+
external deterministic agents

Example:

predictive model:
- “truck failure risk = 82%”
LLM:
- explains situation and proposes actions
eAgent:
- creates work order and schedules repair

So the TF assistant layer is often:

orchestration
explanation
planning
semantic interpretation while specialized predictive models may do the actual numerical prediction.

so customized NNs trained on current state / future state data to predict future state.

just like CNN predict label based on pixels, and TF predicts token based on input tokens.

Yes — exactly. That is the core unifying idea.

A neural network is fundamentally learning:

input patterns
→ output predictions

The meaning of the inputs/outputs changes by application.

CNN

pixels
→ object label

Example:

image
→ “cat”

Transformer

previous tokens
→ next token

Example:

“What is the capital of France?”
→ “Paris”

Predictive model

current state
→ future state

Example:

sensor data
→ machine failure probability

or:

weather conditions
→ tomorrow temperature

So yes: all are basically:

pattern approximation/prediction systems
trained from examples.

The biggest differences are:

architecture
data type
training objective
output format
temporal/context handling

But the underlying principle is very similar.

so is it possible to do a simple predictive NN demo? on my pc with Nvidia gpu

Yes. Very possible.

Best simple demo:

current state → future state

Example:

input: last 20 points of a sine wave
output: next point

That shows predictive AI clearly without huge data.

Your PC/NVIDIA GPU can easily handle it. Use:

WSL2 + Python + PyTorch + CUDA

Minimal demo idea:

train data:
- noisy sine wave
model:
- small MLP or LSTM
task:
- predict next value from previous values
result:
- plot actual future vs predicted future

This maps perfectly to your explanation:

CNN: pixels → label
TF: tokens → next token
Predictive NN: current state → future state

For your wiki/demo series, I’d name it something like:

D1_predictive_nn_current_state_to_future_state.py

or if fitting PAL:

pal_predict_01_future_state.py

Start with a simple MLP, not LSTM. It is easier to explain:

x1…x20 = current/recent state
y1 = predicted next state

Then later upgrade to:

multiple future steps
multiple sensors
anomaly prediction
resource planning

is this how most projectts for predictive (in finance, risk, fraud detection, etc) tdo predictive? custom NNs?

Yes — broadly speaking.

Most real predictive systems are trained on:

current/past state data
→ future/unknown outcome

Examples:

Finance

market history
→ future price movement probability

Fraud detection

transaction patterns
→ fraud probability

Risk systems

customer/company history
→ default/risk score

Predictive maintenance

sensor data
→ failure probability

Logistics

supply-chain state
→ delivery delay prediction

But one important nuance: Many production systems do NOT use giant deep neural networks.

Very often they use:

XGBoost
LightGBM
random forests
logistic regression
ensembles
smaller NNs

because:

cheaper
easier to train
easier to explain
more stable
easier to validate legally/business-wise

Deep NNs are more common when:

huge data
complex patterns
sequences/time series
images/audio/text
high-dimensional latent relationships

exist.

So yes:

custom predictive models are trained for specific domains.

But in enterprise systems:

simple models often outperform overcomplicated deep learning systems.

This actually aligns very strongly with your:

LLM = propose
Code = executes

mindset:

deterministic/simple systems are often preferred when possible
deep AI is used where pattern complexity becomes too large for explicit rules.

26.0524