---
title: "The best approach to compare LLM outputs"
url: https://daily.dev/posts/the-best-approach-to-compare-llm-outputs-qev5ctp3d
source_url: https://portkey.ai/blog/the-best-approach-to-compare-llm-outputs/
type: article
source: "portkey"
published: 2026-02-24T13:48:43.755Z
updated: 2026-03-15T03:42:05.232Z
tags: ["ai-agents", "observability"]
reading_time: 6
upvotes: 1
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# The best approach to compare LLM outputs

**[portkey](https://daily.dev/sources/portkey)** · 6 min read · 1 upvotes · 0 comments

## Summary

A repeatable framework for comparing LLM outputs in production is essential as prompts, models, and routing logic evolve. Manual review and ad-hoc prompting are insufficient at scale due to inconsistency and lack of baselines. Effective evaluation requires tracking metrics across three dimensions: hallucination detection, relevance/correctness, and safety/quality. Metrics can be deterministic (regex, JSON validation) or model-based (LLM-as-a-judge), applied at span, trace, or session granularity. Arize treats evaluation as a continuous operational loop attached to traces, with pre-built and custom evaluators that produce explainable scores. Portkey's AI Gateway complements this by providing routing and orchestration so teams can compare models and prompt versions under identical conditions, connecting evaluation results back to specific routing decisions for actionable iteration.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://portkey.ai/blog/the-best-approach-to-compare-llm-outputs/>

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Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability)

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