Tag: Tool Use

When large language models initially evolved from pure text generators into computational controllers, the primary integration mechanism was natural-language prompt wrapping. Developers instructed models to format their responses as raw JSON strings or markdown-encapsulated code blocks. Downstream application parsers then attempted to deserialize the output using standard regular expressions or JSON deserializers. In production software […]

During the initial phase of AI-assisted automation and language model development, tool evaluation was limited to calling a handful of handcrafted functions. Models were tested on basic calculators, synthetic search mocks, or simple currency converters. While these narrow scenarios verified basic syntax generation, they failed to reflect production software environments. A system evaluated in a […]

The early benchmarking paradigm for large language models centered on static knowledge retrieval and single-turn semantic generation. Models were evaluated on multiple-choice examinations, reading comprehension questions, and isolated function synthesis. While these tests quantified linguistic fluency and factual recall, they failed to assess agency. A model could answer high-level conceptual questions about Linux permissions or […]

As foundation models demonstrated fluent conversational performance and solved curated coding exercises, benchmark designers faced a new challenge: evaluating general-purpose personal and enterprise assistants. Traditional natural language processing benchmarks (such as MMLU or Big-Bench) relied on multiple-choice formats or closed-world question answering. These evaluations measured fact retrieval from static weights rather than the capacity to […]