A Large Language Model (LLM) is a type of artificial intelligence that can understand and generate human-like text based on the patterns and information it has learned from vast amounts of text data.
LLMs are designed to take a string of words as an input and strive to anticipate the subsequent sequence with the highest probability. This is achieved by assigning likelihood values to possible succeeding sequences and subsequently opting for one.
Imagine you have a super smart robot friend that has read lots and lots of books and websites. This robot can now talk and write just like a human because it learned from all that reading. It can help answer questions and have conversations with people – amongst other tasks.
These tasks include engaging in chatbot conversations, translating languages, generating content, and even knowledge retrieval and synthesis.
To understand how LLMs work, it's essential to understand their training process. Training an LLM involves feeding it extensive data, such as books, articles, or web pages. This allows the model to discern patterns and connections between words and phrases.
Once trained, an LLM can generate content based on user-set parameters. For instance, if one wishes to produce an article in Shakespeare's style, they'd provide the model with a prompt, and the LLM would generate the rest of the article based on its understanding of Shakespeare's works
Here are a few types of applications you can build with LLMs, along with some example templates:
By analyzing natural language patterns, LLMs can generate responses akin to human reactions. This is perfect for businesses aiming to offer customer service via chatbots or virtual agents.
Examples:
LLMs are also incredibly competent at knowledge synthesis. For example, you can wire up OpenAI to a 3rd party API and build an intuitive AI-powered chat experience that has access to powerful datasets. Methods like Retrieval Augmented Generation also allow you to add an information retrieval component to the text generation model that LLMs are already good at.
Examples:
- Pinecone Vercel AI SDK Starter
- AI Film Recommendation Generator
- WeatherGPT – ChatGPT Plugin Starter
LLMs can produce essays, articles, or other forms of content that mirror the style of specific authors or genres.
Examples:
- Novel – AI-powered Notion-style editor
By using OpenAI Functions, you can easily add AI-powered user experiences into your application with just a few lines of code.
Examples:
- ChatHN – Chat with the Hacker News API using natural language OpenAI Functions
Models like GPT-4 can accurately translate languages – both human languages (English, French, Chinese) and even programming languages (JavaScript, Python, Rust).
Examples:
Several LLMs have gained prominence in the AI community:
GPT – specifically GPT-3.5 and GPT-4 – are large language models developed by OpenAI.
- GPT-3.5: OpenAI's fastest and cost effective model optimized for chat purposes, but also works well for traditional completions tasks.
- GPT-4: OpenAI's most powerful model that has broad general knowledge and domain expertise allowing it to follow complex instructions in natural language and solve difficult problems accurately.
Learn more about how to use OpenAI Vercel AI SDK to build streaming chat experiences.
Claude – specifically claude-instant-1 and claude-2 – are large language models developed by Anthropic.
claude-instant-1: A faster, cheaper yet still very capable version of Claude, which can handle a range of tasks including casual dialogue, text analysis, summarization, and document comprehension.claude-2: Anthropic's most powerful model that excels at a wide range of tasks from sophisticated dialogue and creative content generation to detailed instruction. It is good for complex reasoning, creativity, thoughtful dialogue, coding,and detailed content creation.
Learn more about how to use Anthropic Vercel AI SDK to build streaming chat experiences.
Command Nightly – specifically command-nightly and command-light-nightly – are large language models developed by Cohere.
command-nightly: An instruction-following conversational model by Cohere that performs language tasks with high quality and reliability while providing longer context compared to generative models.command-light-nightly: A smaller and faster version of Cohere's command model with almost as much capability but improved speed.
Learn more about how to use Cohere Vercel AI SDK to build streaming chat experiences.
LLaMA 2 – specifically llama-2-70b-chat and llama-2-70b – are large language models developed by Meta and hosted on Replicate.
- llama-2-70b-chat: 70 billion parameter model fine-tuned on chat completions. Perfect for building a chat bot with the best accuracy.
- llama-2-70b: 70 billion parameter base model. Perfect for other kinds of language completions, like completing a user’s writing.
Learn more about how to use Replicate Llama2 Vercel AI SDK to build streaming chat experiences.
While LLMs have revolutionized the AI landscape, they come with their set of challenges. Ensuring the accuracy and reliability of the content they generate is paramount. Especially in applications like news generation, where precision is crucial. One way to enhance the reliability of LLMs is by connecting them to dependable data sources, ensuring the content aligns with a company's brand identity. There's also Reinforcement Learning from Human Feedback (RLHF), which is a type of AI that learns to make better decisions by receiving guidance and corrections from humans.
In conclusion, Large Language Models are transforming the way we interact with machines, offering more natural, human-like interactions. As technology continues to evolve, the capabilities and applications of LLMs are bound to expand, paving the way for more advanced and intuitive AI systems.