How Ollama Generates Responses

Post yesterday - 10 Jun 2026 | Updated 10 Jun 2026 | 23

Introduction

When we ask any question from any of ollama Model, We get the response in few seconds, From the perspective of user the process looks very simple:

user → Enter prompt → Generate response

but their is backend whole process which is followed in backend by any AI model which actually helps the model to generate any response. The complete process to generate any response is:

User Prompt
↓
Tokenization
↓
Tokens
↓
Embedding Layer
↓
Transformer Layers
↓
Next Token Prediction
↓
Generated Tokens
↓
Detokenization
↓
Response

Step 1: User Prompt

Firstly, user ask any question to the model.

Example:

Who was Dr.Rajendra Prasad?

Ollama receives the text but AI model do not process the text directly because model is not designed to understand the human language as raw text.

Step 2: Tokenization

Now the user prompt is divided into small parts called Tokens.

Example:

Who
was
Dr.
Rajendra
Prasad
?

Now the divided text are transformed into numbers called tokens.

[1534, 892, 455, 18291, 7721, 30]

in simple words tokenization is process to convert text into machine-readable units.

Step 3: Tokens

Now model do not have any words, now they only have token IDs.

[1534, 892, 455, 18291, 7721, 30]

Now the whole processing will be done on token IDs.

Step 4: Embedding Layer

Model do not works directly on token IDs. So every token is converted into numerical vectors

Flow:

Token → Embedding Layer → Vector

Example:

1534
↓
[0.23, -0.44, 0.82, ...]

Embedding represents the meaning and context of the tokens.

Step 5: Transformer Layers

these are the actual brain of the LLM. Transformer layer analyze the relationship of every token.

Example:

Who was Dr. Rajendra Prasad?

Model will identify

Rajendra Prasad
↓
Historical Person
↓
India
↓
President

It activates the patterns which the model learn during training period. Transformer architecture uses attention mechanism to understand which words are important for sentences.

Step 6: Next Token Prediction

This is the most important step which is actually used to generate the response. LLM not only gains the knowledge It is also responsible to predict the most probable next token.

Example:

Model can internally generate:

Dr. Rajendra Prasad was the

Now the next possible tokens are:

President → 92%
Teacher → 2%
Scientist → 1%
Doctor → 1%

Most probability is of President so the model will choose “President”.

Step 7: Generated Tokens

model generates a token and then predict the next token then follows the continuous process till the response is not fully generated.

Example:

Dr.
Rajendra
Prasad
was
the
first
President
of
India
.

these are the generated tokens.

Step 8: Detokenization

now the generated tokens will be again converted into text for human understanding.

Example:

[874, 2211, 92, 781, ...]

this numbers will converted into text like:

Dr. Rajendra Prasad was the first President of India.

This process is called Detokenization.

Step 9: Final Response

Now the final response will be display

Dr. Rajendra Prasad was the first President of India and served from 1950 to 1962.