Understand How Large Language Models Work
Large Language Models (LLMs) have become the foundation of modern Generative AI applications.
From ChatGPT-style assistants and enterprise search to Retrieval-Augmented Generation (RAG) and AI agents, LLMs are increasingly used to build applications that can understand, generate, summarize, and reason over human language.
But building reliable LLM applications requires more than knowing how to call an API.
You need to understand how language models process text, how Transformers generate predictions, how context affects responses, how prompts influence model behavior, and how LLMs can be adapted and evaluated for real-world applications.
The GeekyCodes LLM learning path takes you from the fundamentals of language modeling to Transformers, prompting, embeddings, fine-tuning, inference, evaluation, and production LLM applications.
[Start Learning LLMs →]
What Are Large Language Models?
A Large Language Model is a machine learning model trained on large amounts of text to learn patterns in language and generate text based on an input context.
At a high level, an LLM application follows:
User Input ↓Tokenization ↓Token IDs ↓Model ↓Next-Token Prediction ↓Decoding / Sampling ↓Generated Response
Modern LLMs are primarily based on the Transformer architecture, which uses attention mechanisms to model relationships between tokens in a sequence.
Understanding this architecture is the foundation for understanding systems such as GPT-style models, encoder models such as BERT, and many other modern language models.
LLM Learning Path
If you’re new to LLMs, follow the concepts in this order:
Natural Language Processing ↓Language Models ↓Tokenization ↓Embeddings ↓Neural Networks ↓Attention ↓Transformers ↓Pretraining ↓Instruction Tuning ↓Prompt Engineering ↓Inference & Decoding ↓ RAG ↓Fine-Tuning ↓LLM Evaluation ↓Production LLM Applications
1. LLM Fundamentals
Start by understanding the concepts behind language models.
Topics
- What is a Language Model?
- What is an LLM?
- AI vs ML vs Deep Learning vs Generative AI
- Traditional Language Models vs LLMs
- Parameters
- Training Data
- Context Window
- Tokens
- Token Probabilities
- Next-Token Prediction
- Model Inference
- Pretraining vs Fine-Tuning
[Explore LLM Fundamentals →](Upcoming)
2. Tokenization
LLMs don’t directly process sentences as humans do.
Text is converted into smaller units called tokens, which are then represented as numerical IDs that the model can process.
Learn:
- What is Tokenization?
- Tokens vs Words
- Subword Tokenization
- Byte Pair Encoding (BPE)
- WordPiece
- SentencePiece
- Token IDs
- Vocabulary
- Special Tokens
- Token Count
- Context Window
Understanding tokenization is especially important when working with LLM APIs, RAG systems, context limits, and inference costs.
[Explore Tokenization →]
3. Embeddings
Embeddings represent information as numerical vectors that capture semantic relationships.
Learn how embeddings are used for:
- Semantic Representation
- Semantic Similarity
- Text Search
- Document Retrieval
- Recommendation Systems
- Clustering
- RAG
- Vector Databases
Topics
- What Are Embeddings?
- Word Embeddings
- Sentence Embeddings
- Contextual Embeddings
- Embedding Models
- Cosine Similarity
- Vector Search
[Explore Embeddings →]
4. Attention Mechanism
Attention is one of the fundamental ideas behind modern Transformer-based models.
Instead of processing every token independently, attention allows a model to determine which other tokens are relevant when representing a particular token.
Learn:
- What is Attention?
- Query, Key and Value
- Scaled Dot-Product Attention
- Self-Attention
- Cross-Attention
- Attention Scores
- Attention Weights
- Multi-Head Attention
[Explore Attention Mechanism →]
5. Transformers
Transformers changed the direction of NLP and became the foundation of modern LLMs.
Learn the architecture step by step:
- Transformer Architecture
- Encoder
- Decoder
- Self-Attention
- Multi-Head Attention
- Feed-Forward Networks
- Residual Connections
- Layer Normalization
- Positional Encoding
- Causal Attention
- Masked Attention
Transformer Models
Explore architectures and models including:
- BERT
- GPT
- T5
- Encoder-Only Models
- Decoder-Only Models
- Encoder-Decoder Models
[Explore Transformers →]
6. How LLMs Are Trained
Training an LLM involves multiple stages.
A simplified pipeline looks like:
Large-Scale Dataset ↓Data Cleaning ↓Tokenization ↓Pretraining ↓Instruction Tuning ↓Preference Alignment ↓Evaluation ↓Deployment
Learn about:
- Pretraining
- Self-Supervised Learning
- Next-Token Prediction
- Training Objectives
- Instruction Tuning
- Supervised Fine-Tuning
- Preference Optimization
- Alignment
- Training Data
- Model Checkpoints
[Explore LLM Training →]
7. Prompt Engineering
Prompts provide instructions and context that guide an LLM’s response.
Good prompt engineering is about more than writing longer prompts. It involves clearly defining the task, providing relevant context, specifying constraints, and controlling the desired output.
Topics
- What is Prompt Engineering?
- Zero-Shot Prompting
- Few-Shot Prompting
- Role Prompting
- Structured Prompts
- Chain-of-Thought
- Prompt Templates
- Output Formatting
- Structured Outputs
- Prompt Optimization
- Prompt Injection
[Explore Prompt Engineering →]
8. Context Windows
An LLM can only process a finite amount of information during a single interaction.
Understanding context windows is important when building applications that work with large documents, conversations, or retrieved information.
Learn:
- What is a Context Window?
- Input vs Output Tokens
- Context Length
- Context Management
- Long-Context Models
- Context Compression
- Context Selection
- Context Overflow
- Managing Conversation History
These concepts become particularly important when designing RAG and agentic applications.
[Explore Context Windows →]
9. Inference & Decoding
During inference, an LLM generates a response one token at a time.
The decoding strategy determines how the next token is selected.
Topics
- LLM Inference
- Logits
- Probabilities
- Temperature
- Top-K Sampling
- Top-P Sampling
- Greedy Decoding
- Beam Search
- Deterministic vs Stochastic Generation
- Maximum Output Tokens
- Latency
- Throughput
Understanding these parameters helps you control the behavior, quality, and cost of LLM applications.
[Explore LLM Inference →]
10. RAG with LLMs
LLMs have limitations.
They may lack access to private or recent information and can generate incorrect information when they don’t have sufficient context.
Retrieval-Augmented Generation addresses this by retrieving relevant information from external sources and providing it to the model.
User Question ↓Query Processing ↓Retrieval ↓Relevant Context ↓Prompt + Context ↓LLM ↓Generated Answer
Learn:
- What is RAG?
- Why LLMs Hallucinate
- Dense Retrieval
- Sparse Retrieval
- BM25
- Hybrid Search
- Reranking
- Parent-Child Retrieval
- Multi-Query Retrieval
- Self-RAG
- Corrective RAG
- Graph RAG
- Agentic RAG
11. Fine-Tuning LLMs
RAG isn’t the right solution for every problem.
Fine-tuning can be used when you need to adapt a model’s behavior, capabilities, or output style for a particular task.
Learn:
- What is Fine-Tuning?
- Pretraining vs Fine-Tuning
- Supervised Fine-Tuning
- Instruction Tuning
- Parameter-Efficient Fine-Tuning
- LoRA
- QLoRA
- PEFT
- Dataset Preparation
- Fine-Tuning Evaluation
- Fine-Tuning vs RAG
Practical Question
Should you use RAG or Fine-Tuning?
Use RAG when the primary requirement is providing the model with external or changing knowledge.
Consider fine-tuning when the primary requirement is adapting model behavior or task performance.
[Explore LLM Fine-Tuning →]
12. LLM Evaluation
A response that sounds convincing isn’t necessarily a correct response.
LLM applications therefore require systematic evaluation.
Evaluate:
- Correctness
- Relevance
- Faithfulness
- Groundedness
- Context Relevance
- Retrieval Quality
- Hallucination
- Response Quality
- Latency
- Cost
Evaluation Techniques
- Human Evaluation
- Automated Evaluation
- LLM-as-a-Judge
- Evaluation Datasets
- Regression Testing
- RAG Evaluation
- Retrieval Metrics
[Explore LLM Evaluation →]
13. LLM Application Architecture
Modern LLM applications often combine multiple components rather than relying on the model alone.
A typical application might look like:
User
│
▼
Application
│
▼
LLM Orchestrator
│ │
▼ ▼
Retrieval Tools
│ │
▼ ▼
Vector Database APIs
│
▼
Reranker
│
▼
LLM
│
▼
Evaluation
│
▼
Response
This architecture forms the foundation for applications such as:
- AI Assistants
- Enterprise Search
- RAG Chatbots
- Document Question Answering
- AI Agents
- Customer Support Systems
- Knowledge Assistants
[Explore LLM Application Architecture →]
14. Production LLM Engineering
Moving an LLM application from a prototype to production introduces additional engineering challenges.
Learn how to handle:
- Latency
- Throughput
- Scalability
- API Rate Limits
- Model Selection
- Cost Optimization
- Caching
- Prompt Management
- Observability
- Logging
- Evaluation
- Security
- Prompt Injection
- Data Privacy
- Reliability
- Failure Handling
A production LLM system should be designed around more than model quality alone.
You need to balance:
Quality +Latency +Cost +Reliability +Security
[Explore Production LLM Engineering →]
15. LLM Frameworks & Tools
Modern LLM applications are commonly built using frameworks and infrastructure for orchestration, retrieval, deployment, and evaluation.
Explore:
LLM & Model Ecosystem
- Hugging Face
- OpenAI APIs
- Open-source LLMs
- Model APIs
Application Frameworks
- LangChain
- LangGraph
Retrieval Infrastructure
- FAISS
- Pinecone
- Milvus
- Vector Databases
Deployment & Infrastructure
- FastAPI
- Docker
- Kubernetes
- Cloud AI Services
The goal is to understand the underlying concepts first, then use frameworks to implement them efficiently.
🛠️ LLM Projects
Put the concepts into practice by building complete applications.
Beginner
LLM Chatbot
Build a simple application that interacts with an LLM through an API.
Intermediate
Document Q&A with RAG
Build a system that allows users to ask questions about their documents.
Documents ↓Chunking ↓Embeddings ↓Vector Database ↓Retrieval ↓LLM ↓Answer
Advanced
Production RAG System
Build a production-oriented RAG pipeline with:
- Hybrid Search
- Reranking
- Metadata Filtering
- Evaluation
- Observability
- Guardrails
Expert
Agentic AI System
Build an AI system capable of:
Goal ↓Planning ↓Tool Selection ↓Tool Execution ↓Observation ↓Reasoning ↓Final Response
[Explore LLM Projects →]
🎯 LLM & Generative AI Interview Preparation
Preparing for an AI Engineer, GenAI Engineer, Machine Learning Engineer, or Data Scientist interview?
Prepare across:
- LLM Fundamentals
- Transformers
- Attention
- Tokenization
- Embeddings
- Prompt Engineering
- RAG
- Vector Databases
- Fine-Tuning
- AI Agents
- LLM Evaluation
- Production LLM Systems
- AI System Design
[Explore GenAI Interview Questions →]
📚 Complete LLM Learning Roadmap
If you’re starting from scratch, follow this path:
Python ↓Machine Learning ↓Deep Learning ↓NLP ↓Language Models ↓Tokenization ↓Embeddings ↓Attention ↓Transformers ↓LLM Training ↓Prompt Engineering ↓Inference ↓RAG ↓Fine-Tuning ↓LLM Evaluation ↓AI Agents ↓Production LLM Engineering ↓LLM System Design
[Follow the Complete LLM Roadmap →]
🔗 Explore Related AI Engineering Topics
RAG Fundamentals
Learn how retrieval systems provide external knowledge to LLMs.
[Explore RAG Fundamentals →]
AI Agents
Learn how LLMs can interact with tools and execute multi-step tasks.
[Explore AI Agents →]
Embeddings & Vector Search
Understand semantic representations and the retrieval infrastructure behind modern AI applications.
[Explore Embeddings & Vector Search →]
LLM Evaluation
Learn how to measure and improve the quality of LLM applications.
[Explore LLM Evaluation →]
Fine-Tuning
Learn how to adapt language models for specific tasks and behaviors.
[Explore LLM Fine-Tuning →]
Frequently Asked Questions
What is an LLM?
A Large Language Model is a machine learning model trained on large amounts of text to learn patterns in language and generate text based on an input context.
How do LLMs work?
Modern LLMs typically use Transformer architectures to process tokenized input and predict subsequent tokens based on the context.
What should I learn before LLMs?
A useful foundation includes Python, machine learning, deep learning, NLP, and basic mathematics. You don’t need to master every topic before beginning LLMs, but understanding these foundations makes the concepts easier to understand.
What is the difference between an LLM and Generative AI?
Generative AI is the broader field of systems that generate content such as text, images, audio, or code. LLMs are models specifically designed around language and are one important component of Generative AI.
What is the difference between RAG and fine-tuning?
RAG provides an LLM with relevant external information at inference time, while fine-tuning changes the model’s learned parameters to adapt its behavior or capabilities.
Start Learning Large Language Models
Large Language Models are at the center of modern Generative AI, but building reliable AI applications requires understanding much more than the model itself.
Follow the GeekyCodes LLM learning path from tokenization and embeddings → attention and Transformers → prompting and inference → RAG and fine-tuning → evaluation → production LLM engineering.
Understand the model. Build the application. Engineer it for production.
[Start with LLM Fundamentals →]
Also If You’re on Medium then follow me there