ai-system-evaluation

What problem does it solve?

This Skill addresses the complex challenge of evaluating AI systems by providing a structured approach to model selection, performance benchmarking, and cost-benefit analysis, enabling informed architectural and deployment decisions.

Core Features & Use Cases

• Model Selection: Guides users through filtering and selecting appropriate AI models based on task requirements, quality thresholds, and constraints.
• Performance Benchmarking: Facilitates the evaluation of models against domain-specific datasets and standard benchmarks for metrics like reasoning, code generation, and knowledge recall.
• Cost & Latency Analysis: Incorporates analysis of operational costs and latency, crucial for real-time applications and budget management.
• Build vs. Buy Decisions: Provides a framework for comparing the trade-offs between using third-party APIs and self-hosting models.
• Use Case: When deciding which LLM to use for a customer support chatbot, this Skill can help evaluate options like GPT-4, Claude 3, or Llama 3 based on their performance on relevant conversational benchmarks, their cost per token, and their expected response times.

Quick Start

Use the ai-system-evaluation skill to compare the performance of models on the GSM-8K benchmark.