Humanloop Blog

Model Context Protocol (MCP) Explained

A guide on the Model Context Protocol (MCP), explaining what MCP is, how it works, its architecture as well as the benefits and challenges you should know.

Conor Kelly

What is LLM Observability and Monitoring?

Learn how LLM monitoring works, why it's important, as well as best practices and important metrics you should know.

Conor Kelly

5 LLM Evaluation Tools You Should Know in 2025

A guide on the LLM evaluation platforms you should know in 2025

Conor Kelly

What is Prompt Management?

Learn what prompt management is, how it works, its benefits and challenges, and how to set it up.

Conor Kelly

Top 5 Vector Databases in 2025

A guide on the top vector database to use for AI agents and applications in 2025

Conor Kelly

AI Is Blurring the Line Between PMs and Engineers

AI is blurring the line between PMs and engineers; now PMs and domain experts can drive product creation through prompt engineering.

Raza Habib

Structured Outputs: Everything You Should Know

Explore Structured Outputs, understand how it works and how to get started on OpenAI, Gemini and Humanloop.

Conor Kelly

Introducing Templates in Humanloop

Jumpstart your AI app development with Humanloop Templates — pre-built workflows, curated datasets, and ready-to-use evaluators designed to accelerate your path to production.

Jordan Burgess

LLM Benchmarks: Understanding Language Model Performance

Learn about key LLM benchmarks, why they should be prioritised for specific tasks, and what metrics should be used to compare LLM performance.

Conor Kelly

8 Retrieval Augmented Generation (RAG) Architectures You Should Know in 2025

Explore the 8 most popular Retrieval Augmented Generation (RAG) architectures, understand their workflows and use cases for building generative AI applications.

Conor Kelly

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