Updates from Adolfo (QE) re text (#556)

* Updates from Adolfo

* small typo update

* updated to address peer review feedback from Chris

* removing extra space

Author: BSynRedhat

lmeval-evaluation-job.adoc

@@ -13,7 +13,7 @@ After the `LMEvalJob` is created, the LM-Eval service runs the evaluation job.
 
 [NOTE]
 --
-TrustyAI does not support non-tabular models. Deploying the TrustyAI custom resource (CR) in a namespace that contains non-tabular models can cause errors within the TrustyAI service.
+Other TrustyAI features (such as bias and drift metrics) do not support non-tabular models (including LLMs). Deploying the TrustyAIService custom resource (CR) in a namespace that contains non-tabular models (such as the namespace where an evaluation job is being executed) can cause errors within the TrustyAI service.
 --
 
 .Sample LMEvalJob object 
@@ -178,7 +178,7 @@ textsynth (TextSynth APIs): Check link:https://github.com/EleutherAI/lm-evaluati
 | `outputs.pvcManaged`
 | Creates an operator-managed PVC to store this job's results. The PVC is named `<job-name>-pvc` and is owned by the `LMEvalJob`. After the job finishes, the PVC is still be available, but it is deleted with the `LMEvalJob`. Supports the following fields:
 
-* `size`: The PVC's size, compatible with standard PVC syntax (e.g. 5Gi)
+* `size`: The PVC's size, compatible with standard PVC syntax (for example, 5Gi)
 
 | `outputs.pvcName`
 | Binds an existing PVC to a job by specifying its name. The PVC must be created separately and must already exist when creating the job.

lmeval-scenarios.adoc

@@ -8,7 +8,7 @@ ifdef::context[:parent-context: {context}]
 
 The procedures below outline different example scenarios which you may find useful for your ML-Eval setup.
 
-== Configuring the ML-eval environment
+== Configuring the LM-eval environment
 
 If the `LMEvalJob` needs to access a model on HuggingFace with the access token, you can set up the `HF_TOKEN` as one of the environment variables for the `lm-eval` container.
 
@@ -20,7 +20,7 @@ If the `LMEvalJob` needs to access a model on HuggingFace with the access token,
 
 .Procedure
 
-Enter the following code:
+To start an evaluation job for a `huggingface` model, apply the following YAML file:
 
 [source]
 ----
@@ -61,6 +61,8 @@ env:
 
 == Custom Unitxt Card
 
+You can also run evaluations using custom unitxt cards. To do this, include the custom unitxt card in JSON format within the LMEvalJob YAML.
+
 .Prerequisites
 * You have logged in to Red Hat OpenShift AI.
 
@@ -251,14 +253,19 @@ spec:
 
 == Using an InferenceService
 
-This example assumes that the vLLM model is already deployed in your cluster.
+To run an evaluation job on an InferenceService which is already deployed and running in your namespace, define your LMEvalJob CR, then apply this CR into the same namespace as your model.
+
 .Prerequisites
 * You have logged in to Red Hat OpenShift AI.
 
 * Your OpenShift cluster administrator has installed OpenShift AI and enabled the TrustyAI service for the data science project where the models are deployed.
 
+* You have a namespace that contains an InferenceService with a vLLM model. This example assumes that the vLLM model is already deployed in your cluster.
+
 .Procedure
+
 * Define your LMEvalJob CR:
+
 [source]
 ----
   apiVersion: trustyai.opendatahub.io/v1alpha1