How it works

Meta’s AI systems that generate text rely on large language models (also called LLMs). LLMs learn language patterns from large amounts of text using a combination of machine learning and the guidance of people who help train the models. LLMs can perform a variety of language-based tasks such as completing a sentence or responding to questions in a conversational way.

There are multiple steps involved in this process, which are described below.

Usage tips

You have multiple options to influence the responses you receive. Here are some tips:

Create your prompt thoughtfully

Your prompt is the most important control you have over the response you will receive from the system. Try different prompts to see how they change the system’s response.

Use clear and specific prompts

For example, asking “What are the best breeds of dogs for families?” will yield better results than asking “What is the best dog?”

Change your AI chat history

The AI system will use your recent conversation history to determine the meaning of your current prompt and what response to give. To alter the responses provided, consider deleting the AI’s copy of your chat history to make a fresh start. Typing /reset-ai will delete a single AI’s copy of your chat history, whereas typing /reset-all-ais will delete all of the AIs’ copies of your chat history across all AIs.

Refine the AI system’s responses

If the AI system’s response is not ideal, provide instructions in one or more steps for how you want the response to change. For example, “Make that shorter and use a friendlier tone,” followed by, “Make it even shorter."

Data usage

A large amount of data is required to teach effective generative AI models, so multiple sources are used for training. These sources include information that is publicly available online and licensed information, as well as information from Meta’s products and services. More details on how we use information from Meta’s products and services are available in our Privacy Policy .

When we collect public information from the internet or license data from other providers to train our models, it may include personal information. For example, a public blog post may include the author’s name and contact information. When we do get personal information as part of the public and licensed data that we use to train our models, we don’t specifically link it to any Meta account. To learn more, visit the Privacy Center .

What to be aware of

Meta’s generative AI technology is still advancing, and there are important limits you should understand about how LLMs work. For example, LLMs may produce responses that are not relevant, accurate or appropriate. Some of the reasons for this are:

• LLMs are language models capable of generating human-like text through predictions based on patterns they learned during development. However, they lack the ability to verify the accuracy or reliability of the responses they produce. Carefully review responses for accuracy. Remember that AIs aren’t human, even though they may respond in ways that seem like real people.

• LLMs may generate responses that include fabricated or entirely fictional information. In other words, the language model "hallucinates" content that does not originate from the data used to train it. Some examples of this may include creating fictional events, people, places or sources of information; providing details or explanations that are not based on facts; or claiming to be a real person.

• LLMs may produce responses that are offensive due to limitations of the data on which they were trained, as well as the “hallucinations” mentioned above. If you see anything that concerns you, provide feedback in the app you’re using.

• The responses an LLM generates may not be up-to-date. Some Meta products may integrate tools such as search engines to deliver the most up-to-date information. Learn more in the Privacy Center .

• Our LLMs were trained primarily on data in English, so performance may vary when using other languages to interact with our generative AI features.