How LLMs Process Prompts
If you’ve ever wondered how large language models (LLMs) understand and respond to your prompts, you’re about to discover the fascinating journey your words take through neural networks. Understanding how LLMs process prompts is crucial for anyone working with AI systems, whether you’re building applications or simply trying to get better responses. When you type a prompt into an LLM, a complex series of transformations begins that converts your text into mathematical representations, processes them through multiple layers, and generates coherent responses.
Understanding the Prompt Processing Pipeline
When you submit a prompt to an LLM, the processing doesn’t happen instantly or magically. The way LLMs process prompts involves several distinct stages that work together to understand context, meaning, and intent. Large language models rely on a sophisticated architecture that breaks down your input text into manageable pieces before reconstructing meaning from patterns learned during training.
The prompt processing pipeline in LLMs consists of tokenization, embedding generation, attention mechanism application, and finally, token generation. Each of these stages plays a critical role in how effectively the LLM processes prompts and produces relevant outputs. Understanding these stages helps you write better prompts and get more accurate responses.
Tokenization: Breaking Down Your Prompt
The first step in how LLMs process prompts is tokenization. Tokenization is the process of breaking down your input text into smaller units called tokens. These tokens can be words, subwords, or even individual characters depending on the tokenization strategy used by the specific LLM.
For example, the sentence “Hello, how are you?” might be tokenized into ["Hello", ",", "how", "are", "you", "?"] using word-level tokenization. However, modern LLMs like GPT models use subword tokenization methods such as Byte Pair Encoding (BPE) or WordPiece. This means a word like “tokenization” might be split into ["token", "ization"] or even smaller units.
Tokenization is essential for how LLMs process prompts because neural networks can only work with numerical data, not raw text. Each token gets assigned a unique numerical ID from the model’s vocabulary. A typical LLM vocabulary contains anywhere from 30,000 to over 100,000 unique tokens.
Let’s look at a simple example of how tokenization works:
# Simple word tokenization example
text = "LLMs process prompts efficiently"
tokens = text.split() # Basic word splitting
print(f"Original text: {text}")
print(f"Tokens: {tokens}")
print(f"Number of tokens: {len(tokens)}")
This basic tokenization gives us individual words, but real LLM tokenizers are much more sophisticated and handle punctuation, special characters, and rare words more intelligently.
Token Embeddings: Converting Tokens to Vectors
After tokenization, the next critical step in how LLMs process prompts is converting tokens into embeddings. Token embeddings are dense vector representations that capture semantic meaning. Each token gets transformed into a high-dimensional vector, typically ranging from 768 to 12,288 dimensions depending on the model size.
These embedding vectors are learned during the training process and encode relationships between tokens. Similar tokens have similar embedding vectors in the high-dimensional space. For instance, the embeddings for “cat” and “dog” would be closer to each other than “cat” and “computer” because they share semantic similarity.
The embedding layer is essentially a lookup table where each token ID maps to its corresponding vector. When LLMs process prompts, they retrieve these pre-trained embeddings for each token in your input. This vector representation allows mathematical operations to be performed on the text data.
Here’s a simplified example of how token embeddings work:
import numpy as np
# Simulating a simple embedding lookup
vocabulary = {"LLMs": 0, "process": 1, "prompts": 2, "using": 3, "embeddings": 4}
embedding_dim = 4 # Simplified dimension
# Create random embeddings (in reality, these are learned during training)
embedding_matrix = np.random.randn(len(vocabulary), embedding_dim)
# Token IDs from our text
token_ids = [0, 1, 2, 3, 4] # "LLMs process prompts using embeddings"
# Look up embeddings for each token
token_embeddings = embedding_matrix[token_ids]
print("Token embeddings shape:", token_embeddings.shape)
print("\nFirst token embedding (LLMs):")
print(token_embeddings[0])
Positional Encoding: Adding Sequence Information
One unique aspect of how LLMs process prompts is positional encoding. Unlike recurrent neural networks that process text sequentially, transformer-based LLMs process all tokens simultaneously. This parallel processing is faster but loses information about word order, which is crucial for understanding language.
Positional encoding solves this problem by adding positional information to the token embeddings. Each position in the sequence gets a unique vector that encodes its location. These positional encodings are added to the token embeddings, allowing the model to understand that “dog bites man” means something different from “man bites dog.”
There are different approaches to positional encoding. The original Transformer architecture used sinusoidal positional encoding with fixed mathematical functions. Newer models often use learned positional embeddings that are optimized during training.
Let’s see a simplified example of positional encoding:
import numpy as np
def create_positional_encoding(max_len, embedding_dim):
"""Create sinusoidal positional encoding"""
position = np.arange(max_len)[:, np.newaxis]
div_term = np.exp(np.arange(0, embedding_dim, 2) * -(np.log(10000.0) / embedding_dim))
pos_encoding = np.zeros((max_len, embedding_dim))
pos_encoding[:, 0::2] = np.sin(position * div_term)
pos_encoding[:, 1::2] = np.cos(position * div_term)
return pos_encoding
# Create positional encoding for a sequence of 5 tokens with 4 dimensions
max_sequence_length = 5
embedding_dimension = 4
pos_encoding = create_positional_encoding(max_sequence_length, embedding_dimension)
print("Positional encoding shape:", pos_encoding.shape)
print("\nPositional encoding for position 0:")
print(pos_encoding[0])
print("\nPositional encoding for position 4:")
print(pos_encoding[4])
The Attention Mechanism: Understanding Context
The attention mechanism is the heart of how LLMs process prompts and arguably the most revolutionary component of modern language models. Attention allows the model to weigh the importance of different tokens when processing each word in your prompt. This mechanism enables LLMs to capture long-range dependencies and understand context across entire sentences or even documents.
When LLMs process prompts using attention, each token can “attend to” every other token in the sequence. The model learns which tokens are most relevant for understanding the current token. For example, in the sentence “The cat sat on the mat because it was tired,” the attention mechanism helps the model understand that “it” refers to “cat” rather than “mat.”
Self-attention computes three vectors for each token: Query (Q), Key (K), and Value (V). These vectors are derived from the token embeddings through learned weight matrices. The attention score between two tokens is calculated by taking the dot product of their Query and Key vectors, which determines how much attention one token should pay to another.
Multi-head attention, used in transformer architectures, runs multiple attention mechanisms in parallel. Each “head” can learn to focus on different aspects of the relationships between tokens. Some heads might focus on syntactic relationships while others capture semantic connections.
Here’s a simplified implementation of the attention mechanism:
import numpy as np
def softmax(x):
"""Compute softmax values for array x"""
exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
def simple_attention(query, key, value):
"""Simplified attention mechanism"""
# Calculate attention scores
scores = np.dot(query, key.T)
# Scale by square root of dimension
d_k = query.shape[-1]
scores = scores / np.sqrt(d_k)
# Apply softmax to get attention weights
attention_weights = softmax(scores)
# Apply attention weights to values
output = np.dot(attention_weights, value)
return output, attention_weights
# Example with 3 tokens and 4 dimensions
num_tokens = 3
embedding_dim = 4
# Simulate Q, K, V matrices
query = np.random.randn(num_tokens, embedding_dim)
key = np.random.randn(num_tokens, embedding_dim)
value = np.random.randn(num_tokens, embedding_dim)
# Compute attention
output, weights = simple_attention(query, key, value)
print("Attention output shape:", output.shape)
print("\nAttention weights (showing which tokens attend to which):")
print(weights)
Feed-Forward Neural Networks and Layer Normalization
After the attention mechanism processes your prompt, the resulting representations pass through feed-forward neural networks. These networks apply non-linear transformations to each token representation independently. The way LLMs process prompts through these layers allows the model to learn complex patterns and relationships in the data.
Feed-forward networks in transformers typically consist of two linear transformations with a non-linear activation function (usually GELU or ReLU) in between. The first layer expands the dimensionality significantly (often by a factor of 4), and the second layer projects it back down to the original size. This expansion and contraction help the model learn rich representations.
Layer normalization is applied both before and after the attention and feed-forward components. This normalization stabilizes training and helps gradients flow through the deep network. Residual connections (skip connections) also add the input of each sub-layer to its output, which helps with training very deep networks.
Here’s a simplified example:
import numpy as np
def relu(x):
"""ReLU activation function"""
return np.maximum(0, x)
def layer_norm(x, epsilon=1e-6):
"""Simple layer normalization"""
mean = np.mean(x, axis=-1, keepdims=True)
std = np.std(x, axis=-1, keepdims=True)
return (x - mean) / (std + epsilon)
def feed_forward(x, w1, b1, w2, b2):
"""Simple feed-forward network"""
# First linear transformation with expansion
hidden = np.dot(x, w1) + b1
# Non-linear activation
hidden = relu(hidden)
# Second linear transformation back to original size
output = np.dot(hidden, w2) + b2
return output
# Example dimensions
input_dim = 4
hidden_dim = 16 # 4x expansion
num_tokens = 3
# Random weights (in reality, these are learned)
w1 = np.random.randn(input_dim, hidden_dim) * 0.1
b1 = np.zeros(hidden_dim)
w2 = np.random.randn(hidden_dim, input_dim) * 0.1
b2 = np.zeros(input_dim)
# Input from previous layer
x = np.random.randn(num_tokens, input_dim)
# Apply feed-forward with residual connection and normalization
normalized_x = layer_norm(x)
ff_output = feed_forward(normalized_x, w1, b1, w2, b2)
output_with_residual = x + ff_output # Residual connection
print("Input shape:", x.shape)
print("Feed-forward output shape:", ff_output.shape)
print("Final output with residual:", output_with_residual.shape)
The Decoder and Token Generation Process
Understanding how LLMs process prompts isn’t complete without knowing how they generate responses. The decoder component takes the processed prompt representations and generates output tokens one at a time. This autoregressive generation process predicts the next token based on all previously generated tokens and the original prompt.
During generation, the model computes probability distributions over its entire vocabulary for the next token. Various sampling strategies can be applied: greedy decoding (always picking the highest probability token), beam search (maintaining multiple candidate sequences), or temperature-based sampling (introducing controlled randomness).
The temperature parameter controls the randomness of generation. Lower temperatures make the model more confident and deterministic, while higher temperatures increase diversity but can reduce coherence. Top-k and top-p (nucleus) sampling are additional techniques that limit the sampling pool to the most likely tokens.
Each generated token gets fed back into the model as part of the input for generating the next token. This continues until the model generates a special end-of-sequence token or reaches a maximum length limit. The way LLMs process prompts during generation involves repeatedly applying the same transformer layers to the growing sequence.
Context Window and Attention Masks
The context window is a crucial limitation in how LLMs process prompts. This window defines the maximum number of tokens the model can consider at once. For example, GPT-3 has a context window of 2,048 tokens, while newer models like GPT-4 can handle up to 32,768 tokens or more.
When your prompt exceeds the context window, the model can only see a truncated portion of your input. This is why breaking down very long documents or providing the most important information first is essential for effective prompt engineering.
Attention masks are used to control which tokens can attend to which other tokens. In causal language models (like GPT), future tokens are masked during training so the model can only attend to previous and current tokens. This ensures the model learns to predict the next token without “cheating” by looking ahead.
Memory and Computation in Prompt Processing
How LLMs process prompts requires significant computational resources. The attention mechanism has quadratic complexity with respect to sequence length, meaning that processing a prompt twice as long requires four times the computation. This is why longer prompts take longer to process and why context window size matters.
The memory requirements also scale with sequence length. Each token’s embedding and the attention scores between all token pairs must be stored in memory during processing. For large models with billions of parameters processing thousands of tokens, this can require gigabytes of GPU memory.
Various optimization techniques help manage these constraints. Key-value caching stores computed attention keys and values during generation, avoiding redundant computation for previously processed tokens. Flash Attention and other efficient attention implementations reduce memory usage and speed up computation.
The Role of Training Data and Fine-tuning
The way LLMs process prompts is fundamentally shaped by their training data. During pre-training, the model learns patterns, relationships, and knowledge from massive text corpora. This training teaches the model not just language structure but also facts about the world, reasoning patterns, and task-solving strategies.
Fine-tuning and instruction tuning further refine how LLMs process prompts for specific tasks. Models fine-tuned on instruction-following datasets learn to better understand and respond to direct commands, questions, and conversational prompts. This additional training helps the model align its responses with human preferences and expectations.
Complete Working Example: Simulating LLM Prompt Processing
Let’s put together all the concepts we’ve discussed into a complete working example that simulates the core components of how LLMs process prompts:
import numpy as np
# Set random seed for reproducibility
np.random.seed(42)
class SimpleLLMProcessor:
"""Simplified LLM prompt processor demonstrating key concepts"""
def __init__(self, vocab_size=1000, embedding_dim=64, num_heads=4, ff_dim=256):
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.num_heads = num_heads
self.ff_dim = ff_dim
# Initialize embedding matrix
self.embeddings = np.random.randn(vocab_size, embedding_dim) * 0.1
# Initialize attention weights (simplified)
self.W_q = np.random.randn(embedding_dim, embedding_dim) * 0.1
self.W_k = np.random.randn(embedding_dim, embedding_dim) * 0.1
self.W_v = np.random.randn(embedding_dim, embedding_dim) * 0.1
# Initialize feed-forward weights
self.W_ff1 = np.random.randn(embedding_dim, ff_dim) * 0.1
self.b_ff1 = np.zeros(ff_dim)
self.W_ff2 = np.random.randn(ff_dim, embedding_dim) * 0.1
self.b_ff2 = np.zeros(embedding_dim)
# Output projection to vocabulary
self.W_out = np.random.randn(embedding_dim, vocab_size) * 0.1
self.b_out = np.zeros(vocab_size)
def tokenize(self, text):
"""Simple tokenization: split by spaces and assign random IDs"""
tokens = text.lower().split()
# Simulate token IDs (in reality, use proper tokenizer)
token_ids = [hash(token) % self.vocab_size for token in tokens]
return tokens, token_ids
def get_embeddings(self, token_ids):
"""Look up embeddings for token IDs"""
return self.embeddings[token_ids]
def positional_encoding(self, seq_len):
"""Create sinusoidal positional encoding"""
position = np.arange(seq_len)[:, np.newaxis]
div_term = np.exp(np.arange(0, self.embedding_dim, 2) *
-(np.log(10000.0) / self.embedding_dim))
pos_encoding = np.zeros((seq_len, self.embedding_dim))
pos_encoding[:, 0::2] = np.sin(position * div_term)
pos_encoding[:, 1::2] = np.cos(position * div_term)
return pos_encoding
def softmax(self, x):
"""Compute softmax"""
exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
def attention(self, x):
"""Simplified single-head attention"""
# Compute Q, K, V
Q = np.dot(x, self.W_q)
K = np.dot(x, self.W_k)
V = np.dot(x, self.W_v)
# Compute attention scores
scores = np.dot(Q, K.T) / np.sqrt(self.embedding_dim)
attention_weights = self.softmax(scores)
# Apply attention to values
output = np.dot(attention_weights, V)
return output, attention_weights
def layer_norm(self, x):
"""Layer normalization"""
mean = np.mean(x, axis=-1, keepdims=True)
std = np.std(x, axis=-1, keepdims=True)
return (x - mean) / (std + 1e-6)
def feed_forward(self, x):
"""Feed-forward network"""
hidden = np.dot(x, self.W_ff1) + self.b_ff1
hidden = np.maximum(0, hidden) # ReLU
output = np.dot(hidden, self.W_ff2) + self.b_ff2
return output
def process_prompt(self, text, verbose=True):
"""Process a prompt through the simplified LLM pipeline"""
if verbose:
print(f"{'='*60}")
print(f"Processing prompt: '{text}'")
print(f"{'='*60}\n")
# Step 1: Tokenization
tokens, token_ids = self.tokenize(text)
if verbose:
print(f"Step 1 - Tokenization:")
print(f" Tokens: {tokens}")
print(f" Token IDs: {token_ids}")
print(f" Number of tokens: {len(tokens)}\n")
# Step 2: Get embeddings
embeddings = self.get_embeddings(token_ids)
if verbose:
print(f"Step 2 - Token Embeddings:")
print(f" Embedding shape: {embeddings.shape}")
print(f" First token embedding (first 5 dims): {embeddings[0, :5]}\n")
# Step 3: Add positional encoding
pos_encoding = self.positional_encoding(len(token_ids))
embeddings_with_pos = embeddings + pos_encoding
if verbose:
print(f"Step 3 - Positional Encoding:")
print(f" Positional encoding shape: {pos_encoding.shape}")
print(f" First position encoding (first 5 dims): {pos_encoding[0, :5]}\n")
# Step 4: Attention mechanism
normalized_input = self.layer_norm(embeddings_with_pos)
attention_output, attention_weights = self.attention(normalized_input)
attention_output = embeddings_with_pos + attention_output # Residual
if verbose:
print(f"Step 4 - Attention Mechanism:")
print(f" Attention output shape: {attention_output.shape}")
print(f" Attention weights shape: {attention_weights.shape}")
print(f" Attention weight matrix (showing token interactions):")
print(f" {attention_weights}\n")
# Step 5: Feed-forward network
normalized_attention = self.layer_norm(attention_output)
ff_output = self.feed_forward(normalized_attention)
ff_output = attention_output + ff_output # Residual
if verbose:
print(f"Step 5 - Feed-Forward Network:")
print(f" Feed-forward output shape: {ff_output.shape}")
print(f" Output (first token, first 5 dims): {ff_output[0, :5]}\n")
# Step 6: Project to vocabulary for next token prediction
logits = np.dot(ff_output[-1], self.W_out) + self.b_out
probabilities = self.softmax(logits)
predicted_token_id = np.argmax(probabilities)
top_5_tokens = np.argsort(probabilities)[-5:][::-1]
if verbose:
print(f"Step 6 - Token Generation:")
print(f" Logits shape: {logits.shape}")
print(f" Predicted next token ID: {predicted_token_id}")
print(f" Prediction probability: {probabilities[predicted_token_id]:.4f}")
print(f" Top 5 predicted token IDs: {top_5_tokens}")
print(f" Top 5 probabilities: {probabilities[top_5_tokens]}\n")
return {
'tokens': tokens,
'token_ids': token_ids,
'embeddings': embeddings,
'attention_weights': attention_weights,
'final_representation': ff_output,
'predicted_token_id': predicted_token_id,
'token_probabilities': probabilities
}
# Create and test the LLM processor
processor = SimpleLLMProcessor(vocab_size=1000, embedding_dim=64, num_heads=4, ff_dim=256)
# Process a sample prompt
prompt = "how LLMs process prompts efficiently"
results = processor.process_prompt(prompt)
print(f"{'='*60}")
print("Summary of Prompt Processing")
print(f"{'='*60}")
print(f"Input prompt: '{prompt}'")
print(f"Number of processing steps: 6")
print(f"Total tokens processed: {len(results['tokens'])}")
print(f"Embedding dimensions: {results['embeddings'].shape[1]}")
print(f"Attention captured {results['attention_weights'].shape[0]}x{results['attention_weights'].shape[1]} token interactions")
print(f"\nThe model has successfully processed the prompt through:")
print(f" ✓ Tokenization")
print(f" ✓ Embedding lookup")
print(f" ✓ Positional encoding")
print(f" ✓ Self-attention mechanism")
print(f" ✓ Feed-forward transformation")
print(f" ✓ Next token prediction")
Output:
============================================================
Processing prompt: 'how LLMs process prompts efficiently'
============================================================
Step 1 - Tokenization:
Tokens: ['how', 'llms', 'process', 'prompts', 'efficiently']
Token IDs: [284, 156, 738, 923, 475]
Number of tokens: 5
Step 2 - Token Embeddings:
Embedding shape: (5, 64)
First token embedding (first 5 dims): [-0.04486212 -0.08329623 -0.01582594 0.11445796 -0.04713901]
Step 3 - Positional Encoding:
Positional encoding shape: (5, 64)
First position encoding (first 5 dims): [0. 1. 0. 1. 0. ]
Step 4 - Attention Mechanism:
Attention output shape: (5, 64)
Attention weights shape: (5, 5)
Attention weight matrix (showing token interactions):
[[0.20311845 0.19744078 0.20121033 0.19782842 0.20040202]
[0.20118803 0.20073631 0.19969991 0.19975127 0.19862448]
[0.19899095 0.20160891 0.20114502 0.19963644 0.19861868]
[0.20035273 0.20038028 0.1988849 0.20107068 0.19931141]
[0.20048912 0.19892206 0.19991289 0.20052037 0.20015556]]
Step 5 - Feed-Forward Network:
Feed-forward output shape: (5, 64)
Output (first token, first 5 dims): [-0.25123935 -0.08748082 -0.13195362 0.17476896 0.02419568]
Step 6 - Token Generation:
Logits shape: (1000,)
Predicted next token ID: 739
Prediction probability: 0.0024
Top 5 predicted token IDs: [739 294 816 523 167]
Top 5 probabilities: [0.00242687 0.00216943 0.00208312 0.00188436 0.00183265]
============================================================
Summary of Prompt Processing
============================================================
Input prompt: 'how LLMs process prompts efficiently'
Number of processing steps: 6
Total tokens processed: 5
Embedding dimensions: 64
Attention captured 5x5 token interactions
The model has successfully processed the prompt through:
✓ Tokenization
✓ Embedding lookup
✓ Positional encoding
✓ Self-attention mechanism
✓ Feed-forward transformation
✓ Next token prediction
This comprehensive example demonstrates the complete pipeline of how LLMs process prompts from raw text input to generating predictions for the next token. You can see each transformation step, the shapes of intermediate representations, and how attention weights capture relationships between tokens in your prompt.
Key Takeaways About LLM Prompt Processing
Understanding how LLMs process prompts gives you powerful insights into optimizing your interactions with these models. The multi-stage pipeline converts your text into mathematical representations, applies sophisticated attention mechanisms to capture context, and generates responses token by token through learned probability distributions.
The efficiency of how LLMs process prompts depends on factors like context window size, model architecture, and computational resources available. Writing clear, well-structured prompts that provide necessary context within the token limit helps the model generate more accurate and relevant responses.
Modern LLMs use transformer architectures that process prompts in parallel rather than sequentially, enabling faster processing but requiring positional encoding to maintain word order information. The attention mechanism allows these models to understand long-range dependencies and complex relationships between different parts of your prompt.
By understanding these underlying mechanisms, you can craft better prompts, anticipate model limitations, and leverage the strengths of LLMs more effectively in your applications and workflows. The way LLMs process prompts continues to evolve with new architectures and optimization techniques, but the fundamental principles of tokenization, embedding, attention, and generation remain central to how these powerful models understand and respond to human language.