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ai-improving-accuracy

Define custom metrics, run evaluations, and apply optimization strategies like BootstrapFewShot.

11|1|Updated Feb 8, 2026
Search Tags:#optimization#metrics#benchmark#evaluate#evaluation-framework#ai-improvement
One-click install
npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-improving-accuracy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-improving-accuracy
Source: https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills/tree/main/skills/ai-improving-accuracy
Command: npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-improving-accuracy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Measure and improve how well your AI performs by defining evaluation metrics, running tests, and iterating on prompts and data.

Core Features & Use Cases

  • Define metrics to score AI outputs (accuracy, consistency, and factuality) across tasks.
  • Run automated evaluations and compare baselines to quantify improvements.
  • Apply optimization strategies (BootstrapFewShot, MIPROv2, GEPA, BootstrapFinetune) to enhance performance in production-like settings.
  • Use-case example: verify a chatbot's answers on a QA set and drive systematic improvements through metric-driven feedback.

Quick Start

Define a metric, run an evaluation on your development set, and apply an optimizer to improve results.

Frequently Asked Questions about ai-improving-accuracy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate AI accuracy using custom metrics?▼

Evaluate AI accuracy by defining custom metrics to score outputs on accuracy, consistency, and factuality, then running automated evaluations against a baseline to quantify improvements.

What is the best way to improve chatbot performance on a QA dataset?▼

Improve chatbot performance by applying optimization strategies like BootstrapFewShot, MIPROv2, GEPA, and BootstrapFinetune to iteratively refine prompts and data after initial evaluation.

Can I use this evaluation framework for code helpers and reasoning tasks?▼

Yes, the evaluation framework supports AI assistants, chatbots, and code helpers across QA tasks, reasoning, and generation, allowing you to measure and optimize performance in production-like settings.

How do I define custom metrics to score AI outputs for factuality?▼

Define custom metrics within the evaluation framework to test AI outputs for accuracy, consistency, and factuality, enabling systematic improvements through metric-driven feedback loops.

What optimization strategies are available for AI improvement?▼

Available optimization strategies include BootstrapFewShot, MIPROv2, GEPA, and BootstrapFinetune, which are applied to enhance AI performance after running automated evaluations and comparing baselines.

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