This past development cycle focused on enhancing the AI Fill feature and improving our system's observability. We implemented improved contextual awareness for AI Fill's search capabilities, ensuring that searches within specific fields remain accurate and efficient by intelligently managing the information provided to the AI. This refinement ensures that core field identities are always preserved for precise searches, while optimizing the augmentation process for broader queries.

Furthermore, we've made significant strides in enhancing our observability by integrating ISO timestamps and detailed call durations into AI Fill's logging. This allows for more precise debugging and performance analysis by enabling the correlation of start and end times for tool calls and AI fill operations. These updates will provide greater clarity into system behavior and aid in faster issue resolution.

Additionally, we conducted exploratory work on handling new data structures, specifically investigating the "Faroe materials" table and developing an evaluation runner for AI Fill. These initiatives are foundational for future feature development and represent our ongoing commitment to expanding Lotta Supply's data handling and analytical capabilities. Minor performance optimizations and bug fixes were also addressed to ensure a stable and reliable user experience.