Utsav Khandelwal

✨ Maxim AI February 2025 Update

✨ Maxim AI February 2025 Update

Feature spotlight 🚀 Agent simulation Teams today spend countless hours manually going back and forth with their agents to assess quality and identify failure modes. And it’s not a one-time thing— unintended regressions happen all the time, whether due to model updates or optimizations based on user feedback. Introducing Maxim’

✨ Maxim AI January 2025 Updates

✨ Maxim AI January 2025 Updates

Feature spotlight 🔍 Evaluate and monitor AI agents using Maxim It is a constant challenge for AI teams to debug, monitor, and understand what's going wrong in the complex workflows of AI agents, which involve multiple steps, i.e., LLM calls, tool calls, data retrievals, etc. To ensure the

Maxim AI x MongoDB

Build a RAG application using MongoDB and Maxim AI

What is RAG? Retrieval-augmented generation (RAG) is a process designed to enhance the output of a large language model (LLM) by incorporating information from an external, authoritative knowledge base. This approach ensures that the responses generated by the LLM are not solely dependent on the model's training data.

Maxim AI - Product Updates, December 2024 ✨

Maxim AI - Product Updates, December 2024 ✨

Feature spotlight 👩‍🏫 Use human annotation queues for data curation To optimize the quality and performance of AI applications, teams need to continuously inspect traces, annotate examples, and use these logs as datasets for evaluation, fine-tuning, or in-context learning. This is a manually intensive process where various components are siloed and