Utsav Khandelwal

Utsav Khandelwal

Evaluating the Quality of Healthcare Assistants using Maxim AI

Evaluating the Quality of Healthcare Assistants using Maxim AI

Introduction Healthcare assistants are changing the way patients and clinicians interact. For patients, these tools offer easy access to timely advice and guidance, improving overall care and satisfaction. For clinicians, they reduce administrative tasks, allowing more time for patient care while providing real-time knowledge and data to support informed decision-making.

Evaluating the Quality of Clinical Documentation Using Maxim AI

Evaluating the Quality of Clinical Documentation Using Maxim AI

Medical note-taking and scribing assistants have eased the administrative burden on clinicians by ambiently generating clinical notes, allowing them to spend less time documenting and more time with patients. In high-stakes clinical environments, it is essential to generate clear, accurate, and safe notes based on multi-turn interactions between clinicians and

✨ Public APIs, xAI support, MCP, and more

✨ Public APIs, xAI support, MCP, and more

Feature spotlight 🔗 Prompt chains: Now more powerful Maxim’s revamped Prompt Chains lets you prototype every step of your complex agentic workflow with greater clarity and control—right from our more intuitive UI. Key highlights: * 🔀 Create parallel chains to execute concurrent or conditional tasks. * 🔁 Flexibly transfer data between ports—choose

✨ 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