-
Prompt Learning: Using Natural Language to Optimize LLM Systems
Your customers expect better and more consistent results than your AI agent can deliver. You manually tweak a prompt, test…
-
Chain-of-Thought Prompting: A Guide for LLM Applications and Agents
When Google researchers asked GPT-3 to solve grade-school math problems, the model answered 17.9 percent of the problems correctly. When…
-
Prompt Tuning: Parameter-Efficient Optimization for Agentic AI Systems
You’ve built an agentic system that coordinates retrieval, reasoning, and response generation across multiple specialized tasks. Now you need to…
-
MIPRO: The Optimizer That Brought Science to Prompt Engineering
You know the routine: Write your first prompt, and then spend hours manually tweaking prompts, testing variations, and documenting what…
-
GEPA: Why Reflection-Based Optimization Is Replacing Reinforcement Learning for AI Agents
Your multi-hop reasoning agent fails 55 percent of the time. You spend three days tweaking prompts by adjusting the phrasing,…
Author: Jamie Gillenwater
Get started today for free.
You don’t need a credit card to sign up, and your Comet account comes with a generous free tier you can actually use—for as long as you like.