As artificial intelligence systems evolve, one of the most important technical frontiers is the ability to handle longer inputs. From analyzing legal contracts and research papers to powering autonomous agents that review entire codebases, modern AI increasingly needs to process far more information at once. Traditional transformer-based models, however, are constrained by fixed context windows that limit how much text they can “see” in a single pass. Recent innovations...
As organizations increasingly integrate large language models into production systems, monitoring tool invocation and function calling behavior has moved from a technical curiosity to a mission‑critical requirement. Platforms like Helicone are emerging to provide deep observability into how AI systems use external tools, execute function calls, consume tokens, and impact cost and reliability. Without structured logging and analytics, teams operate blind—unable to diagnose failures, optimize performance, or maintain compliance.
TLDR:...
As artificial intelligence systems evolve, one of the most important technical frontiers is the ability to handle longer inputs. From analyzing legal contracts and...
As organizations increasingly integrate large language models into production systems, monitoring tool invocation and function calling behavior has moved from a technical curiosity to...
As artificial intelligence becomes deeply embedded in modern applications, from customer support bots to real-time fraud detection systems, the challenge is no longer simply...
Large language models (LLMs) are powerful, but they can be expensive to run at scale. Every prompt, completion, and embedded text consumes tokens—and tokens...