Large Language Models (LLMs) have transformed the way we interact with technology. From chatbots to code assistants, these powerful models are now accessible to developers of all skill levels. In this guide, we will walk through the fundamentals of LLMs and how you can start building with them today.
What Are Large Language Models?
LLMs are neural networks trained on massive amounts of text data. They learn patterns in language — grammar, facts, reasoning, and even coding — by predicting the next word in a sequence. Models like GPT-4, Claude, and Llama have billions of parameters that capture these patterns.
Understanding Tokenization
Before text reaches an LLM, it is broken down into tokens — small chunks of text that the model can process. Understanding tokenization is crucial because it affects everything from input limits to costs. For example, the word 'understanding' might be split into 'under' and 'standing' depending on the tokenizer.
Prompt Engineering
The way you phrase your input (the prompt) dramatically affects the output quality. Key techniques include: providing clear instructions, giving examples (few-shot learning), specifying the desired format, and using system prompts to set behavior.
Fine-Tuning vs RAG
When you need domain-specific knowledge, you have two main approaches: fine-tuning (training the model further on your data) and Retrieval-Augmented Generation (RAG), which provides relevant context at query time. RAG is often more practical and cost-effective for most use cases.
Getting Started
The easiest way to start is with the OpenAI API or open-source alternatives like Llama via Ollama. Set up a simple project, experiment with prompts, and gradually build more complex applications. The TechConnect AI/ML workshop series covers all of these topics in depth.