Evaluation

IntraLLM AI provides a built-in evaluation workflow for comparing models based on your organization’s real usage, using simple thumbs up/down feedback and optional arena-style comparisons to build a personalized leaderboard.

Overview

IntraLLM AI includes an in-product evaluation workflow that helps your team compare models based on real usage, not generic public benchmarks.

Where to find Evaluations

You can manage and review evaluation features in:

Admin Settings → Evaluations

From there, admins can view model performance and access leaderboard-style results derived from user feedback.

Why evaluate models?

Teams often have access to many capable models (GPT-class models, LLaMA-family models, and others). While public benchmarks and leaderboards can be useful, they’re not always reliable indicators of performance for your workload.

Real-world results vary depending on:

  • The domain and data your team works with
  • Prompt style and conversational context
  • Output tone, clarity, and consistency
  • Potential evaluation bias (including models trained on popular benchmark datasets)

IntraLLM AI addresses this by collecting feedback during normal chat usage—no complex offline scoring required.

Summary

  • Why evaluations matter: model performance depends on context; generic leaderboards rarely match your requirements.
  • What IntraLLM AI provides: an in-product evaluation workflow using thumbs up/down ratings.
  • How scoring works: ratings contribute to a personalized leaderboard when comparisons are available.
  • Two evaluation modes: arena-style model comparisons or normal chat usage with ratings.
  • Future-facing workflow: rated chat snapshots can be used later for model refinement and fine-tuning (feature availability may vary by version).

Why public evaluation is not enough

Public evaluations can fall short for several reasons:

  • Not tailored to your use case: general benchmarks may not reflect your organization’s workflows.
  • Dataset exposure risk: some models may be trained on evaluation datasets, reducing fairness.
  • Style and usability differences: a model may score well overall but still miss the tone, structure, or operational standards your team needs.

Personalized evaluation with IntraLLM AI

How it works:

  • During chats, users provide a thumbs up or thumbs down on responses.
  • Ratings influence the leaderboard when there is a comparable alternative response (a sibling response).
  • Admins can review evaluation outcomes and model rankings in Admin Settings → Evaluations.
  • When a response is rated, the system can capture a snapshot of the chat for future improvement workflows.

Note: Snapshot-based fine-tuning and downstream training workflows may be under active development depending on your deployment and version.

Two ways to evaluate models

1. Arena Model

Arena Mode randomly selects from a pool of available models to support fair, unbiased comparison.

How to use:

  • Select the Arena Model option in the model selector.
  • Select "+" to add the second model.
  • Chat as usual.
  • To affect rankings, ensure you have a sibling response available.

Scoring note:

  • In head-to-head comparisons, upvoting one response may automatically downvote the competing response. Upvote only when the selected answer is clearly better.

2. Normal interaction

You can also evaluate models during standard usage without switching to arena mode.

How to use:

  • Chat normally with a selected model.
  • Provide thumbs up/down ratings on responses where appropriate.
  • If you want ratings to influence rankings, create a sibling response by:
    • Regenerating a reply, or
    • Switching models and asking the same question again

Leaderboard

As feedback accumulates, you can review model rankings in Admin Settings → Evaluations.

Key concepts:

  • The leaderboard uses an Elo-style rating approach (similar to competitive ranking systems).
  • Rankings become more meaningful as the number of comparisons increases.
  • Results reflect performance in your environment, not public benchmarks.

Topic-based reranking

For teams working across multiple domains (e.g., customer support, creative writing, technical troubleshooting), topic tagging enables more granular insights.

Automatic tagging

IntraLLM AI can attempt to automatically tag chats by topic. Depending on the model and content, tags may be incomplete or inaccurate.

Manual tagging

When rating a response, users can add or edit tags based on the conversation context. This improves:

  • Topic-specific reranking accuracy
  • Domain-level model selection
  • Visibility into strengths and weaknesses by category

Chat snapshots and future fine-tuning workflows

When a response is rated, IntraLLM AI can capture a snapshot of the conversation. These snapshots may later support:

  • Fine-tuning internal models
  • Building higher-quality evaluation datasets
  • Improving future model routing and selection

Note: Availability and implementation details can vary by version and deployment configuration.

Privacy and data control

Evaluation data is stored on your instance. Nothing is shared externally unless you explicitly enable community sharing or opt in to external workflows. This design supports privacy, governance, and data autonomy requirements.

Summary

IntraLLM AI evaluation aims to:

  • Make model comparison simple and operationally practical
  • Help teams identify the best model for their specific needs
  • Provide a feedback loop that can support continuous improvement over time

Use Arena Mode for systematic head-to-head comparisons, or rate responses during normal usage to build a personalized ranking based on real work.