The temperature parameter controls randomness during token selection. A higher temperature broadens the probability distribution and permits less-likely tokens to be selected, producing more varied or creative responses. A lower temperature concentrates selection on the highest-probability tokens, resulting in responses that are more focused, concrete, and consistent across repeated requests. Microsoft's prompt- engineering guidance explicitly states that higher temperatures produce more divergent output, whereas lower values reduce randomness.
Therefore, decreasing the temperature-typically toward zero for automated validation scenarios-is the appropriate configuration change. It reduces wording and structural variation without changing the prompt or intentionally limiting the information available to the model. Although probabilistic model execution does not guarantee byte-for-byte identical output in every circumstance, this is the correct parameter for minimizing variability among the available choices.
Increasing max_tokens only raises the maximum response length and does not control sampling randomness.
Stop sequences define where generation terminates; removing them can change response length but does not make responses deterministic. Increasing temperature would directly increase variability.
Study Guide alignment: Optimize and operationalize generative AI systems-tune generation behavior through prompt engineering and model parameters, and evaluate agent behavior for production reliability.

RAG injects fresh, frequently changing knowledge at inference time by retrieving relevant content from an index and grounding the model's answer in it, which is exactly the "chat with your data" pattern this scenario describes. Microsoft's guidance is direct on the boundary between the two techniques.
Use fine-tuning when you need to change model behavior, style, or task performance, rather than add fresh knowledge.

For a support agent requiring deep multi-step reasoning, long context processing, and strict grounding in internal documents, the GPT-5.5 (or GPT-5.5-Pro) model from the Azure AI Foundry model catalog is the best choice.
Here is how GPT-5.5 directly addresses the requirements:
Deep Multi-Step Reasoning: These frontier models use deliberate reasoning and iterative planning before generating a response. This allows the agent to decompose complex support queries, analyze alternatives, and prevent hallucinations without requiring heavy custom prompt engineering.
Grounded, Accurate Responses:
Rather than doing this alone, pair the model with Foundry IQ connected to your Azure AI Search indices. Foundry IQ's agentic retrieval engine will pull exactly the right context, allowing GPT-5.5 to synthesize the answer and cite the original documentation.
Long Context Handling:
GPT-5.5 models support massive context windows, allowing them to ingest extensive previous conversational turns alongside detailed internal documentation in a single pass without losing track of important rules.
Note:
GPT-5.5 is a large language model (LLM).While it is a flagship LLM built on OpenAI's advanced transformer architecture, it also features natively omnimodal capabilities that allow it to process both text and images seamlessly within a single unified framework. However, when choosing between the specific categories provided, its primary core classification is a Large Language Model (LLM).
Key Details About GPT-5.5
Core Architecture: Large Language Model (LLM) built by OpenAI.
Primary Focus: Highly optimized for complex reasoning, multi-step problem solving, coding, and autonomous agentic workflows.
Input/Output Capabilities: Supports text and image inputs with text-based outputs.
Reference:
https://developers.openai.com/api/docs/guides/reasoning

Configure hybrid search , which executes full-text and vector queries within the same Azure AI Search request. The full-text component searches the product-name and product-code fields through the lexical index, providing the precision required for exact or near-exact identifiers. Microsoft specifically identifies product codes and other specialized terms as scenarios that frequently perform better with keyword search.
The vector component compares the embedding of the user's natural-language query with the embeddings stored for product descriptions. This retrieves semantically similar products even when the query and indexed description do not share the same literal words. Azure AI Search runs the full-text and vector searches in parallel and combines their result sets by using Reciprocal Rank Fusion, returning one unified ranking to the Foundry agent.
Keyword-only search would preserve exact matching but perform poorly for conceptual or paraphrased descriptions. Vector-only search supports semantic similarity but can miss precise product codes and rare identifiers. Semantic search alone reranks text-search results using language understanding; it does not replace the vector query required to use the existing embedding field.
Study Guide alignment: configure semantic, hybrid, and vector search for grounding, choose an appropriate retrieval method, and connect retrieval pipelines to agent tools .