---
title: "Discussion on \"Why Enterprise AI Needs an Evaluation Strategy Before It Goes Live\" | Hashnode"
description: "Discussion on \"Why Enterprise AI Needs an Evaluation Strategy Before It Goes Live\". Learn how enterprises can evaluate AI accuracy, reliability, safety, and business impact before deployment and throughout the AI lifecycle."
url: "https://hashnode.com/posts/why-enterprise-ai-needs-an-evaluation-strategy-before-it-goes-live/6ad057c6aab428c262426d5c"
---

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# [Why Enterprise AI Needs an Evaluation Strategy Before It Goes Live](https://technologyposts.hashnode.dev/why-enterprise-ai-needs-an-evaluation-strategy-before-it-goes-live?utm_source=hashnode&utm_medium=feed)

Learn how enterprises can evaluate AI accuracy, reliability, safety, and business impact before deployment and throughout the AI lifecycle.

Oct 15 · 6 min read · technologyposts.hashnode.dev  
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-   [#enterprise-ai](https://hashnode.com/tag/enterprise-ai)
-   [#ai](https://hashnode.com/tag/ai)

## Written by

[Sampada Sharma](https://hashnode.com/@techytech)

@techytech

  
[View profile](https://hashnode.com/@techytech)

Working in Tech

I work in tech and have interest in areas like Gen AI, cloud etc.

## Responses 3

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[Kartik N V J K](https://hashnode.com/@kartik-nvj)

[Oct 15](https://hashnode.com/posts/why-enterprise-ai-needs-an-evaluation-strategy-before-it-goes-live/6ad057c6aab428c262426d5c/comment/6ad12d28ceecf4a5d026b155)

The point that each use case needs its own definition of an acceptable result is where I've watched eval efforts stall, because teams reach for generic metrics first. For summaries I check whether anything appears that is absent from the source, the same criterion you list, and that one check catches more for me than any fluency score. How do you decide which cases to rerun before a prompt change ships?

[Darius Tran](https://hashnode.com/@dariustran)

[Oct 15](https://hashnode.com/posts/why-enterprise-ai-needs-an-evaluation-strategy-before-it-goes-live/6ad057c6aab428c262426d5c/comment/6ad07c6d51210ccf0ec34103)

The gap between a good demo and real work usually comes down to the test set. Demos use clean inputs, real work has the messy ones.

One thing that helped me: build the eval set from cases that actually failed or got escalated, rather than from examples someone invented.

Do you run evals offline before launch, or in shadow mode on live traffic first?

[Sampada Sharma](https://hashnode.com/@techytech)

[Oct 15](https://hashnode.com/posts/why-enterprise-ai-needs-an-evaluation-strategy-before-it-goes-live/6ad057c6aab428c262426d5c/comment/6ad07c6d51210ccf0ec34103#reply-6ad07d75a4eb64b63fb1c227)

That’s a good point. Failed and escalated cases often reveal edge cases that synthetic examples miss. I’d start with offline evals to establish a baseline, then use shadow mode to see how the system handles real-world inputs before letting it take action.