Skip to main content

48 posts tagged with "genai"

Systematic Prompt Optimization for OpenAI Agents with GEPA

Prompt engineering is critical for building reliable AI systems, but it's fraught with challenges. Manual iteration is time-consuming, lacks systematic guarantees for improvement, and often yields inconsistent results. It is even harder if your system has multiple different prompts. To address this, automatic joint prompt optimization algorithms such as GEPA and MIPRO have been developed. While DSPy has made these optimization techniques accessible within its framework, applying them to other agent frameworks—such as OpenAI Agents SDK, LangChain, or Pydantic AI—has historically required significant integration effort. A prompt registry is the foundation for this workflow, providing the versioned prompt management that optimization builds on.

Rapidly Prototype and Evaluate Agents with Claude Agent SDK and MLflow

Rapidly Prototype and Evaluate Agents with Claude Agent SDK and MLflow

Beyond Manually Crafted LLM Judges: Automate Building Domain-Specific Evaluators with MLflow

Beyond Manually Crafted LLM Judges: Automate Building Domain-Specific Evaluators with MLflow

Building and Managing an LLM-based OCR System with MLflow

Building and Managing an LLM-based OCR System with MLflow

Assessment-focused UIs in MLflow

Assessment-focused UIs in MLflow

MLflow Meets TypeScript: Debug and Monitor Full-Stack AI Applications with MLflow

MLflow Meets TypeScript: Debug and Monitor Full-Stack AI Applications with MLflow

Announcing MLflow 3

Announcing MLflow 3

Automatically find the bad LLM responses in your LLM Evals with Cleanlab

Automatically find the bad LLM responses in your LLM Evals with Cleanlab

Practical AI Observability: Getting Started with MLflow Tracing

Practical AI Observability: Getting Started with MLflow Tracing

Beyond Autolog: Add MLflow Tracing to a New LLM Provider

Beyond Autolog: Add MLflow Tracing to a New LLM Provider