Decoding the LLM Pipeline: How Large Language Models Work in 8 Steps

Author(s): Ecem Karaman

Originally published on Towards AI.

Introduction: Making Sense of the Black Box

On the surface, LLMs seem pretty straightforward — you type something in, and they generate a response. Simple input, simple output. But under the hood, it’s a complex chain of transformations — raw text gets broken into numbers, passed through layers of neural computations, and somehow, the model produces something that sounds remarkably human.

At its core, it all comes down to one thing: predicting the next word.

In this post, I’ll break down that process into eight clear steps within the LLM pipeline, giving you a full mental model of how an LLM processes language from start to finish.

At a high level, this pipeline can be broken down into eight key stages:

1. Input Processing — Turning raw text into structured data the model can work with.
2. Neural Network Processing — Applying layers of attention and transformations to extract meaning.
3. Output Processing & Decoding — Converting those computations back into text.
4. Training & Optimization — How the model learns patterns from vast amounts of data.
5. Memory & Context Handling — Managing long conversations and keeping track of previous inputs.
6. Customization & Inference — Fine-tuning and adapting models for real-world applications.
7. Evaluation & Safety — Ensuring accuracy, reducing biases, and improving reliability.
8. Scaling & Future Improvements — How these models are getting smarter and more efficient.

💡 This is the first post in a series where I’ll break down the LLM pipeline into eight steps. In the upcoming posts, I’ll deep dive into each step, reverse engineering the underlying mechanisms, the mathematical foundations, key model architectures, and real-world implementations.

✅ Want to go deeper? Check out my GitHub for hands-on Jupyter notebooks alongside this guide.

🔹 Step 1: Input Processing (How Text is Prepared for the Model)

LLMs don’t “read” text — they process numbers. Tokenization is the first critical step in that process.

Goal: Convert raw user text into a format the model can understand.

Raw text → tokenization → token IDs → structured input for model

📌 Key Steps:

• Raw Text → Preprocessed Text: Cleans input (removes unnecessary spaces, normalizes casing, formats special characters).
• Text → Tokens: Splits input into words/subwords using a tokenizer (BPE, WordPiece, Unigram).
• Tokens → Token IDs: Maps each token to a unique numerical ID based on the model’s vocabulary.
• Token IDs → Chat Templates (if applicable): Structures input into system, user, and assistant roles for conversational AI.
• Token IDs → Model Input: Packs tokens into a format with padding, truncation, and attention masks.
• Send to Neural Network: Encoded input is passed into the model’s embedding layer for further processing.

import tiktoken

tokenizer = tiktoken.encoding_for_model("gpt-4")
text = "I want to learn about LLMs"
tokens = tokenizer.encode(text)

print("Token IDs:", tokens) # [40, 1390, 311, 4048, 922, 445, 11237, 82]
print("Decoded Tokens:", [tokenizer.decode([t]) for t in tokens])
#['I', ' want', ' to', ' learn', ' about', ' L', 'LM', 's']

🔹 Step 2: Neural Network Processing (How LLMs Think)

This is where the model learns meaning, context, and relationships between words.

Goal: Transform token embeddings using layers of self-attention & neural computations.

Token IDs → embeddings → positional encoding → self-attention → transformed representations

📌 Key Steps:

• EmbeddToken IDs → Embeddings: Maps token IDs to dense vector representations via an embedding matrix.
• Embeddings → Positional Encoding: Adds order information using sinusoidal or learned encodings.
• Positional Encodings → Self-Attention Mechanism: Computes relationships between all tokens via query-key-value (QKV) matrices.
• Self-Attention Output → Multi-Head Attention: Uses multiple attention heads to capture different word relationships.
• Multi-Head Attention → Feedforward Layers: Applies non-linear transformations to refine learned representations.
• Final Representations → Next Processing Step: Outputs processed embeddings for decoding and token prediction.

🔹 Step 3: Output Processing & Decoding (Generating the Next Token)

Once the model processes the input, it predicts the next token by converting internal representations into a probability distribution over possible words.

Goal: Convert numerical representations back into human-readable text.

Final hidden state → logits → softmax → next token selection → detokenized text

📌 Key Steps:

• Final Hidden State → Logits: Converts processed embeddings into raw (unnormalized) scores for each possible next token.
• Logits → Probabilities: Applies the softmax function to transform raw scores into a probability distribution.
• Next Token Selection (Decoding Strategies): Picks the next token using methods like greedy decoding, beam search, or sampling.
• Appending Token to Output: Adds the selected token to the growing sequence.
• Token ID → Text (Detokenization): Converts the generated token ID back into human-readable text.
• Repeat Until Completion: The process loops until an end-of-sequence token or max length is reached.

🔹 Step 4: Training & Optimization (How LLMs Learn)

Training is where an LLM learns understand the language and recognize patterns— the bigger the dataset, the smarter the model.

Goal: Train the model using massive datasets and optimization techniques.

Pretraining (unsupervised) → fine-tuning (supervised) → RLHF → loss calculation → weight updates

📌 Key Steps:

• Pretraining (Unsupervised): Predict missing words in large-scale text (next-word prediction, masked tokens).
• Fine-Tuning (Supervised): Train on labeled data for task-specific improvements (e.g., summarization, Q&A).
• RLHF (Reinforcement Learning): Optimize responses based on human feedback using reward models.
• Loss Function: Measure prediction errors (cross-entropy loss, KL divergence).
• Backpropagation: Compute gradients and adjust model weights based on errors.
• Optimization (Gradient Descent): Update weights iteratively to minimize loss (Adam, SGD).

🔹 Step 5: Memory & Context Handling (How LLMs “Remember” Things)

This step helps LLMs track conversations, recall relevant details, and improve long-form responses!

Goal: Retain context across multiple turns in a conversation.

Context window limits → attention optimizations → retrieval (RAG) → prompt structuring

📌 Key Steps:

• Context Window: Limits how many tokens the model can process at once (e.g., 4K, 8K, 100K tokens).
• Sliding Window Attention: Retains recent tokens by shifting the window forward as new tokens are added.
• Long-Context Handling: Uses attention mechanisms like ALiBi, RoPE, or memory-efficient transformers.
• Retrieval-Augmented Generation (RAG): Fetches relevant external documents to supplement model responses.
• Prompt Engineering: Structures inputs to guide the model’s recall and maintain coherence.
• Memory Persistence (Fine-Tuning vs. External Tools): Adjust weights (fine-tuning) or use external memory (vector databases).

🔹 Step 6: Customization & Inference (How LLMs Are Deployed & Used)

This is where LLMs are tailored, optimized and deployed in real-world apps — like AI chatbots, coding assistants, and search engines.

Goal: Adapt pre-trained models to specific use cases.

Fine-tuning → instruction tuning → prompt tuning → LoRA → optimized real-time responses

📌 Key Steps:

• Pretrained Model → Fine-Tuned Model: Adapts a general LLM for specialized tasks (e.g., coding, medical AI).
• Prompt Tuning (Soft Prompts): Adjusts embeddings instead of weights for lightweight adaptation.
• LoRA & Adapter Layers: Efficiently fine-tunes only specific model layers, reducing computation costs.
• Instruction Tuning: Trains models to follow structured prompts more accurately.
• Inference Pipeline: Converts user input → tokenization → model processing → response generation.
• Latency Optimization: Uses quantization, distillation, and batching for faster real-time responses.

🔹 Step 7: Evaluation & Safety (How We Measure & Improve LLMs)

LLMs can hallucinate, reinforce biases, or produce unreliable outputs, so evaluation is critical.

Goal: Measure performance, detect biases, and ensure reliable, ethical AI outputs.

Perplexity → coherence metrics → bias/toxicity checks → hallucination detection → robustness testing

📌 Key Evaluation Metrics:

• Perplexity & Accuracy: Lower perplexity = better predictions; accuracy measures correctness in structured tasks.
• BLEU, ROUGE, METEOR (Text Quality): Evaluate fluency and coherence in generated text.
• Bias & Fairness Checks: Detect and mitigate harmful biases using specialized datasets.
• Toxicity & Safety Filters: Apply classifiers to prevent harmful or offensive outputs.
• Hallucination Detection: Identify incorrect or fabricated information using factual consistency models.
• Red-Teaming & Adversarial Testing: Stress-test models against manipulative or malicious prompts.

🔹 Step 8: Scaling & Future Improvements (What’s Next for LLMs?)

This step focuses on making LLMs faster, more scalable, and adaptable for future AI applications!

Goal: Enhance efficiency, increase context length, and expand capabilities (e.g., multimodal AI)

Model scaling → efficiency optimizations → longer context → multimodal AI → edge deployment

• Model Scaling (Bigger Networks): Increase parameters (GPT-3 → GPT-4) for better performance.
• Efficient Architectures: Use sparse models (Mixture of Experts, Transformer-XL) to reduce compute costs.
• Longer Context Windows: Extend token limits using memory-efficient attention mechanisms (ALiBi, RoPE).
• Quantization & Pruning: Reduce model size while maintaining accuracy for faster inference.
• On-Device & Edge AI: Optimize smaller models (GPT-4 Turbo, LLaMA) for local deployment.
• Multimodal Capabilities: Expand beyond text (vision-language models, speech integration).

At first glance, LLMs might seem like a black box, but when broken down into these 8 core steps, it becomes clear how they process, learn, and generate intelligent responses. From turning raw text into structured inputs to predicting the next token and continuously improving through training and optimization, every stage plays a crucial role in making these models work.

This post provided a high-level overview, but in the next articles, I’ll deep dive into each step, unpacking the technical details, the math behind it, and real-world implementations.

If you’ve enjoyed this article:

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Published via Towards AI