LLM Limitations: Key Limitations to Be Aware Of

Knowledge Cutoff – LLMs are trained on data up to a fixed point in time, meaning they have no awareness of events or developments that occur after that date. Math Errors – Due to their token-prediction architecture, LLMs are unreliable at precise calculations; always delegate arithmetic to a calculator tool or Python interpreter for accurate results. Character Recognition Errors – Tokenisation causes LLMs to process text as chunks rather than individual characters, making them prone to mistakes in tasks like counting letters, reversing strings, or identifying specific characters.

Key Takeaways

Never rely on an LLM for current or real-time information without retrieval augmentation. Offload all calculations to a dedicated tool such as a Python interpreter. Avoid tasks requiring exact character-level processing unless paired with deterministic tooling.