This paper investigates the ability of large language models (LLMs) to encode information about the truthfulness of their generated outputs. It first shows that truthfulness information is concentrated in specific tokens within the generated text, particularly those representing the exact answer. The paper then demonstrates that while LLMs can encode truthfulness, this information is not universally encoded across all tasks and datasets, suggesting that LLMs possess "skill-specific" truthfulness mechanisms. The research further explores the types of errors LLMs make, identifying patterns and predicting these errors based on internal representations. Finally, the paper reveals a discrepancy between the model's internal representation of truthfulness and its external behavior, indicating that LLMs may encode the correct answer but still generate an incorrect one. This research provides valuable insights into the limitations and potential of LLMs, highlighting the importance of understanding their internal representations to improve error detection and mitigation.