Eval doc updates (#322)

1. Add a link to the RAGAS nv metrics prompts
2. Add a note that the RAGAS nv metrics prompts are not tunable. The user can instead use the "Tunable RAG Evaluator" or add their own "Custom Evaluator"
3. Add a note on the recommended max_tokens for Trajectory Evluator.

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Authors:
  - Anuradha Karuppiah (https://github.com/AnuradhaKaruppiah)

Approvers:
  - David Gardner (https://github.com/dagardner-nv)

URL: #322

Author: AnuradhaKaruppiah

docs/source/workflows/evaluate.md

@@ -115,7 +115,7 @@ llms:
     model_name: meta/llama-3.1-70b-instruct
     max_tokens: 8
 ```
-For these metrics, it is recommended to use 8 tokens for the judge LLM.
+For these metrics, it is recommended to use 8 tokens for the judge LLM. The judge LLM returns a floating point score between 0 and 1 for each metric where 1.0 indicates a perfect match between the expected output and the generated output.
 
 Evaluation is dependent on the judge LLM's ability to accurately evaluate the generated output and retrieved context. This is the leadership board for the judge LLM:
 ```
@@ -126,6 +126,8 @@ Evaluation is dependent on the judge LLM's ability to accurately evaluate the ge
 ```
 For a complete list of up-to-date judge LLMs, refer to the [RAGAS NV metrics leadership board](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_nv_metrics.py)
 
+For more information on the prompt used by the judge LLM, refer to the [RAGAS NV metrics](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_nv_metrics.py). The prompt for these metrics is not configurable. If you need a custom prompt, you can use the [Tunable RAG Evaluator](../reference/evaluate.md#tunable-rag-evaluator) or implement your own evaluator using the [Custom Evaluator](../extend/custom-evaluator.md) documentation.
+
 ### Trajectory Evaluator
 This evaluator uses the intermediate steps generated by the workflow to evaluate the workflow trajectory. The evaluator configuration includes the evaluator type and any additional parameters required by the evaluator.
 
@@ -138,9 +140,11 @@ eval:
       llm_name: nim_trajectory_eval_llm
 ```
 
-A judge LLM is used to evaluate the trajectory based on the tools available to the workflow.
+A judge LLM is used to evaluate the trajectory produced by the workflow, taking into account the tools available during execution. It returns a floating-point score between 0 and 1, where 1.0 indicates a perfect trajectory.
+
+It is recommended to set `max_tokens` to 1024 for the judge LLM to ensure sufficient context for evaluation.
 
-The judge LLM is configured in the `llms` section of the configuration file and is referenced by the `llm_name` key in the evaluator configuration.
+To configure the judge LLM, define it in the `llms` section of the configuration file, and reference it in the evaluator configuration using the `llm_name` key.
 
 ## Workflow Output
 The `aiq eval` command runs the workflow on all the entries in the `dataset`. The output of these runs is stored in a file named `workflow_output.json` under the `output_dir` specified in the configuration file.