formating

Author: lgayhardt

articles/ai-foundry/concepts/evaluation-evaluators/agent-evaluators.md

@@ -57,9 +57,9 @@ model_config = AzureOpenAIModelConfiguration(
 
 We support AzureOpenAI or OpenAI [reasoning models](../../../ai-services/openai/how-to/reasoning.md) and non-reasoning models for the LLM-judge depending on the evaluators:
 
-| Evaluators | Reasoning Models as Judge (ex: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (ex: gpt-4.1, gpt-4o, etc.) | To enable |
-|------------|-----------------------------------------------------------------------------|-------------------------------------------------------------|-------|
-| `Intent Resolution` / `Task Adherence` / `Tool Call Accuracy` / `Response Completeness`) | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
+| Evaluators | Reasoning Models as Judge (example: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (example: gpt-4.1, gpt-4o, etc.) | To enable |
+|--|--|--|--|
+| `Intent Resolution`, `Task Adherence`, `Tool Call Accuracy`, `Response Completeness` | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
 | Other quality evaluators| Not Supported | Supported | -- |
 
 For complex evaluation that requires refined reasoning, we recommend a strong reasoning model like `o3-mini` and o-series mini models released afterwards with a balance of reasoning performance and cost efficiency.

articles/ai-foundry/concepts/evaluation-evaluators/general-purpose-evaluators.md

@@ -45,9 +45,9 @@ model_config = AzureOpenAIModelConfiguration(
 
 We support AzureOpenAI or OpenAI [reasoning models](../../../ai-services/openai/how-to/reasoning.md) and non-reasoning models for the LLM-judge depending on the evaluators:
 
-| Evaluators | Reasoning Models as Judge (ex: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (ex: gpt-4.1, gpt-4o, etc.) | To enable |
+| Evaluators | Reasoning Models as Judge (example: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (example: gpt-4.1, gpt-4o, etc.) | To enable |
 |------------|-----------------------------------------------------------------------------|-------------------------------------------------------------|-------|
-| `Intent Resolution` / `Task Adherence` / `Tool Call Accuracy` / `Response Completeness`) | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
+| `Intent Resolution`, `Task Adherence`, `Tool Call Accuracy`, `Response Completeness` | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
 | Other quality evaluators| Not Supported | Supported | -- |
 
 For complex evaluation that requires refined reasoning, we recommend a strong reasoning model like `o3-mini` and o-series mini models released afterwards with a balance of reasoning performance and cost efficiency.

articles/ai-foundry/concepts/evaluation-evaluators/rag-evaluators.md

@@ -57,7 +57,7 @@ We support AzureOpenAI or OpenAI [reasoning models](../../../ai-services/openai/
 
 | Evaluators | Reasoning Models as Judge (example: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (example: gpt-4.1, gpt-4o, etc.) | To enable |
 |--|--|--|--|
-| `Intent Resolution` / `Task Adherence` / `Tool Call Accuracy` / `Response Completeness` | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
+| `Intent Resolution`, `Task Adherence`, `Tool Call Accuracy`, `Response Completeness` | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
 | Other quality evaluators| Not Supported | Supported | -- |
 
 For complex evaluation that requires refined reasoning, we recommend a strong reasoning model like `o3-mini` and o-series mini models released afterwards with a balance of reasoning performance and cost efficiency.

articles/ai-foundry/how-to/develop/agent-evaluate-sdk.md

@@ -170,9 +170,9 @@ converted_data = converter.convert(thread_id, run_id)
 
 And that's it! `converted_data` contains all inputs required for [these evaluators](#evaluators-supported-for-evaluation-data-converter). You don't need to read the input requirements for each evaluator and do any work to parse the inputs. All you need to do is select your evaluator and call the evaluator on this single run. We support AzureOpenAI or OpenAI [reasoning models](../../../ai-services/openai/how-to/reasoning.md) and non-reasoning models for the judge depending on the evaluators:
 
-| Evaluators | Reasoning Models as Judge (ex: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (ex: gpt-4.1, gpt-4o, etc.) | To enable |
-|------------|-----------------------------------------------------------------------------|-------------------------------------------------------------|-------|
-| `Intent Resolution` / `Task Adherence` / `Tool Call Accuracy` / `Response Completeness`) | Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
+| Evaluators | Reasoning Models as Judge (example: o-series models from Azure OpenAI / OpenAI) | Non-reasoning models as Judge (example: gpt-4.1, gpt-4o, etc.) | To enable |
+|--|--|--|--|
+| `Intent Resolution`, `Task Adherence`, `Tool Call Accuracy`, `Response Completeness`| Supported | Supported | Set additional parameter `is_reasoning_model=True` in initializing evaluators |
 | Other quality evaluators| Not Supported | Supported | -- |
 
 For complex tasks that require refined reasoning for the evaluation, we recommend a strong reasoning model like `o3-mini` or the o-series mini models released afterwards with a balance of reasoning performance and cost efficiency.

articles/ai-foundry/how-to/develop/evaluate-sdk.md

@@ -52,6 +52,7 @@ Built-in quality and safety metrics take in query and response pairs, along with
 
 Built-in evaluators can accept query and response pairs, a list of conversations in JSON Lines (JSONL) format, or both.
 
+
 **Quality Evaluators:**
 
 | Evaluator | Conversation & single-turn support for text | Conversation & single-turn support for text and image | Single-turn support for text only | Requires `ground_truth` | Supports [agent inputs](./agent-evaluate-sdk.md#agent-messages) |
@@ -69,6 +70,7 @@ Built-in evaluators can accept query and response pairs, a list of conversations
 | `ResponseCompletenessEvaluator` | ✓ | | ✓ | ✓ | |
 | `QAEvaluator` | | | ✓ | ✓ | |
 
+
 **Natural Language Processing (NLP) Evaluators:**
 
 | Evaluator | Conversation & single-turn support for text | Conversation & single-turn support for text and image | Single-turn support for text only | Requires `ground_truth` | Supports [agent inputs](./agent-evaluate-sdk.md#agent-messages) |
@@ -80,6 +82,7 @@ Built-in evaluators can accept query and response pairs, a list of conversations
 | `BleuScoreEvaluator` | | | ✓ | ✓ | |
 | `MeteorScoreEvaluator` | | | ✓ | ✓ | |
 
+
 **Safety Evaluators:**
 
 | Evaluator | Conversation & single-turn support for text | Conversation & single-turn support for text and image | Single-turn support for text only | Requires `ground_truth` | Supports [agent inputs](./agent-evaluate-sdk.md#agent-messages) |
@@ -94,6 +97,7 @@ Built-in evaluators can accept query and response pairs, a list of conversations
 | `CodeVulnerabilityEvaluator` | | | ✓ | | ✓ |
 | `IndirectAttackEvaluator` | ✓ | | | | ✓ |
 
+
 **Azure OpenAI Graders:**
 
 | Evaluator | Conversation & single-turn support for text | Conversation & single-turn support for text and image | Single-turn support for text only | Requires `ground_truth` | Supports [agent inputs](./agent-evaluate-sdk.md#agent-messages) |