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How shall I refer to the documents and the context in the prompt when using the Azure RAG-QA framework?

I use [Azure OpenAI RAG-QA](https://learn.microsoft.com/en-us/azure/ai-services/openai/use-your-data-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python) (aka "bring our data"):

[![enter icription here][1]][1]

  [1]: https://i.sstatic.net/Z4vVUxXm.png

which I call via e.g.:

```
import os
import pprint

from openai import AzureOpenAI
#from azure.identity import DefaultAzureCredential, get_bearer_token_provider

endpoint = os.getenv("ENDPOINT_URL", "https://[redacted].openai.azure.com/")
deployment = os.getenv("DEPLOYMENT_NAME", "[redacted GPT engine name]")
search_endpoint = os.getenv("SEARCH_ENDPOINT", "https://[redacted].search.windows.net")
search_key = os.getenv("SEARCH_KEY", "[redacted key]")
search_index = os.getenv("SEARCH_INDEX_NAME", "[redacted]")

# token_provider = get_bearer_token_provider(
#     DefaultAzureCredential(),
#     "https://cognitiveservices.azure.com/.default")

client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_version="2024-05-01-preview",
    api_key='[redacted key]'
)
# azure_ad_token_provider=token_provider,

completion = client.chat.completions.create(
    model=deployment,
    messages=[
        {
            "role": "user",
            "content": "How can I sort a Python list?"
        }],
    max_tokens=800,
    temperature=0,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0,
    stop=None,
    stream=False,
    extra_body={
        "data_sources": [{
            "type": "azure_search",
            "parameters": {
                "endpoint": f"{search_endpoint}",
                "index_name": "[redacted]",
                "semantic_configuration": "default",
                "query_type": "vector_semantic_hybrid",
                "fields_mapping": {},
                "in_scope": True,
                "role_information": "You are an AI assistant that helps people find information.",
                "filter": None,
                "strictness": 5,
                "top_n_documents": 10,
                "authentication": {
                    "type": "api_key",
                    "key": f"{search_key}"
                },
                "embedding_dependency": {
                    "type": "deployment_name",
                    "deployment_name": "[redacted]"
                }
            }
        }]
    }
)
pprint.pprint(completion)
```

It retrieves 10 documents (let's call that the context), then uses them to answer the question in the prompt (`"content": "How can I sort a Python list?"` in the example), following the usual RAG-QA pattern. I'd like the prompt to refer the context e.g.:

- "don't add any info not explicitly written in the context"
- "don't use more than 2 documents from the context"
- "copy-paste as much as possible from the context and write a fewer new words as possible"

But how am I supposed to refer to the documents and the context in the prompt? What's the proper term that the LLM understands (which partly/mostly depends on how the context is given to the LLM by that Azure OpenAI RAG-QA framework)?