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• Internet connectivity
  • Make sure your connection works.
• Token limit reached
  • Simplify the prompt or break it down into smaller chunks.
  • Try asking for a “summary” first, to capture key pointsm before expanding further. This may allow you to work on smaller chunks at a time, making it less likely to reach a max token limit.
  • If the above do not work, let the administrator know. They may be able to adjust max limits, other pertinent settings, and advise you on which LLM may be best suited to your query while also preventing a token limit issue.

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1. Local Language Model: This term typically refers to a language model that operates on a local or on-premises system, meaning it runs on your own computer or server, rather than relying on external cloud-based services. Local language models can be fine-tuned and customized to suit specific needs or security requirements, and they might be used for various tasks, such as text generation, translation, or chatbots. They are often used when data privacy and control are paramount.
2. OpenAI: OpenAI is an artificial intelligence research organization that has developed various language models, including GPT-3 (and possibly newer versions as of my last knowledge update in January 2022). OpenAI’s models are known for their general language understanding and text generation capabilities. OpenAI provides access to its models through APIs, allowing developers to integrate their models into applications and services.

So, the key difference is that “OpenAI” refers to the organization developing the language models, while “local language model” refers to where and how the model is deployed and used. You can use an OpenAI model both locally and through cloud-based services, depending on your needs and preferences.

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LLMs are trained on a large variety of data. However, to answer a question on a specific topic, or to draw conclusions from a specific topic, they need to be supplied with the context of that topic.

AI agents are an intuitive way to provide that context.

Selecting a agent provides the context of that agent to the LLM. This lets users build agents for specific topics, and then use them to answer questions about that topic, without being “polluted” by information that is not in the agent. This prevents “hallucinations” and answers that are out-of-context.

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Retrieval-augmented generation (RAG) fills in this gap. Instead of trying to piece together a response based on all the information the LLM was trained with, the LLM can now “ask” a specific dataset that knows the up-to-date and topical information.
A WildcatGPT AI agent, or “brain” is such a dataset. You feed it all the specific information you are interested in. The LLM can then find the information in the RAG data, and return meaningful answers from it. The responses will be as up-to-date as the data you have fed the brain, and it can provide the sources, in order to support the validity of the responses.

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