Pseo Llm Visibility

What it does

This skill provides visibility into the internal workings of Large Language Models (LLMs). It allows users to inspect intermediate states, track token probabilities, and understand how an LLM arrives at a given output. This is useful for debugging complex prompts or understanding model behavior in detail.

When to use it

• Debugging Complex Prompts: When an LLM produces unexpected results with intricate prompts, this skill can help pinpoint the source of the issue.
• Understanding Model Reasoning: Gain insight into why a model made a specific decision by observing its internal thought process.
• Educational Purposes: Demonstrate how LLMs function to students or colleagues learning about AI and natural language processing.
• Model Evaluation: Assess the reliability and transparency of an LLM's responses by examining intermediate steps.

Key capabilities

• Inspect token probabilities at each step of generation.
• View internal states during LLM operation.
• Track the evolution of the model’s understanding.

Example prompts

• "Show me the token probabilities for the first 5 tokens when generating 'The quick brown fox.'"
• "Trace the internal state as you answer: What is the capital of France?"
• "Explain your reasoning step-by-step, showing intermediate states."

Tips & gotchas

This skill requires a foundational understanding of LLM architecture and tokenization. The output can be verbose; consider limiting the scope (e.g., number of tokens) to avoid overwhelming results.