LLM
How to implement budget limits and alerts in LLM applications
Learn how to implement budget limits and alerts in LLM applications to control costs, enforce usage boundaries, and build a scalable LLMOps strategy.
LLM
observability
Using metadata for better LLM observability and debugging
Learn how metadata can improve LLM observability, speed up debugging, and help you track, filter, and analyze every AI request with precision.
LLM
What is AI interoperability, and why does it matter in the age of LLMs
Learn what AI interoperability means, why it's critical in the age of LLMs, and how to build a flexible, multi-model AI stack that avoids lock-in and scales with change.
LLM
Bringing GenAI to the classroom
Discover how top universities like Harvard and Princeton are scaling GenAI access responsibly across campus and how Portkey is helping them manage cost, privacy, and model access through Internet2’s service evaluation program.
LLM
What is LLM Orchestration?
Learn how LLM orchestration manages model interactions, cuts costs, and boosts reliability in AI applications. A practical guide to managing language models with Portkey
LLM Gateway
What is an LLM Gateway?
An LLM Gateway simplifies managing large language models, enhancing the performance, security, and scalability of real-world AI applications.
Chain of Thought
Chain-of-Thought (CoT) Capabilities in O1-mini and O1-preview
Explore O1 Mini & O1 Preview models with Chain-of-Thought (CoT) reasoning, balancing cost-efficiency and deep problem-solving for complex tasks.
AI
Understanding RAG: A Deeper Dive into the Fusion of Retrieval and Generation
Retrieval-Augmented Generation (RAG) models represent a fascinating marriage of two distinct but complementary components: retrieval systems and generative models. By seamlessly integrating the retrieval of relevant information with the generation of contextually appropriate responses, RAG models achieve a level of sophistication that sets them apart in the realm of artificial