large language model (LLM)

A large language model (LLM) is a neural network trained on very large text corpora using self-supervised objectives, such as next-token prediction or masked modeling, to predict missing or future tokens, enabling broad language understanding and generation.

Modern LLMs are typically transformer-based and are pre-trained once on large unlabeled text corpora, then adapted to downstream tasks via prompting, fine-tuning, or intermediate techniques, such as instruction tuning or reinforcement learning from human feedback.

They support multiple capabilities, such as text generation, summarization, question answering, translation, and code generation. However, they also inherit limitations from their training data and modeling process, including factual inaccuracies, biases, limited context window, and failures in reasoning or coherence.

Related Resources

Tutorial

Build an LLM RAG Chatbot With LangChain

Large language models (LLMs) have taken the world by storm, demonstrating unprecedented capabilities in natural language tasks. In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j.

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For additional information on related topics, take a look at the following resources:

• Simon Willison: Using LLMs for Python Development (Podcast)
• Leveraging Documents and Data to Create a Custom LLM Chatbot (Podcast)
• Measuring Bias, Toxicity, and Truthfulness in LLMs With Python (Podcast)
• First Steps With LangChain (Course)
• Build an LLM RAG Chatbot With LangChain (Quiz)