Counting LLM Tokens

In the world of LLMs, someone eventually pays for “tokens.” But tokens are not necessarily equivalent to words. Understanding the relationship between words and tokens is critical to grasping how language models like GPT-4 process text.

While a simple word like “cat” may be a single token, a more complex word like “unbelievable” might be broken down into multiple tokens such as “un,” “believ,” and “able.” By converting text into these smaller units, language models can better understand and generate natural language, making them more effective at tasks like translation, summarization, and conversation.

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LLM Prompt Injection – Try this example.

As professionals working on AI projects, you might find this example of LLM Prompt Injection particularly relevant to your work. I’ve been involved in several AI projects, and I’d like to share one specific instance of LLM Prompt Injection that you can experiment with right away.

With the rapid deployment of AI features in the enterprise, it’s crucial to maintain the overall security of your creations. This example specifically addresses LLM Prompt Injection, one of the many aspects of LLM security. 

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AI on IBM Power

Theme: Use your Power9 IBM Power resources for AI data processing tasks

By now, most have heard the saying:

If you are not using AI, you are behind.

For companies using IBM Power, especially Power9, which is commonplace throughout the industry, here is an idea of how you might use your existing or easily accessible cloud-based Power resources to help you jumpstart your journey to AI.

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