> ## Documentation Index > Fetch the complete documentation index at: https://docs.neutrinoapp.com/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Routing tags allow you to gather observability metrics for specific sections of your AI application, explore how different models perform on your use-case, and get the highest quality responses while balancing for cost and latency for your LLM queries. ### Tagging your queries To create a tag you must send an actual query request where the model is denoted by tag:name-of-your-tag. The query will return an actual response which is using gpt-4-turbo by default. ```python theme={null} from openai import OpenAI client = OpenAI( base_url="https://router.neutrinoapp.com/api/engines", api_key="" ) response = client.chat.completions.create( # Instead of a specific model, denote a tag with the tag: keyword model="tag:name-of-tag", # examples: "tag:coding_agent", "tag:chatbot2" messages = [ {"role": "system", "content": "You are a helpful AI assistant. Your job is to be helpful and respond to user requests."}, {"role": "user", "content": "What is a Neutrino?"}, ], ) print(f"Optimal model: {response.model}") print(response.choices[0].message.content) ``` ### Using Neutrino Dashboard Now you can go to platform.neutrinoapp.com to monitor queries and perform exploration to identify the best LLMs for your tag title

### Change response model Your responses can be generated from a specific model of your choosing or the Neutrino Intelligent LLM Auto Router. By default your queries will be processed using GPT-4-Turbo title

### Exploration Exploration will be triggered automatically once there are enough diverse queries collected. This roughly equates to around \~500 queries. **Selecting LLMs to explore** Before exploration is automatically triggered you can select which LLMs you would like to explore on the Exploration configuration tab title

**Customizing evaluation rubric** After responses are generated for all queries in the test bank, a custom evaluation rubric is created. You can edit this rubric to include or change metrics for the LLM-as-a-Judge system. title

**Starting LLM-as-a-Judge evaluations** You have to manually trigger the evaluation system in the exploration tab title

**Identifying the best LLMs for your use-case** Once the evaluations are done, you will recieve an email to see the results in the exploration tab. title