Key Parameters in LLM APIs | Generated by AI
Absolutely—no tables, just clear and neat explanations. Here’s a streamlined breakdown of the key parameters available when working with LLM APIs (like OpenAI’s or Copilot-like systems):
Common Parameters in LLM “Chat Completion” APIs
These are the key components you’ll typically pass when making a request—either to OpenAI, Azure OpenAI, or systems derived from them (like Copilot in some contexts):
1. model
Specifies which language model to use (e.g., gpt-4, gpt-3.5-turbo, etc.). It dictates quality, speed, and cost.
2. messages
An array of chat messages structured like:
[
{"role": "user", "content": "Write a short story"}
]
Each message has a role (user, assistant, or system) and content.
3. temperature
Controls randomness:
- Low (0–0.3): Very deterministic; safe for factual or precise responses.
- Medium (0.4–0.7): Balanced—useful for general writing or code tasks.
- High (0.8–1.2): More creative; ideal for brainstorming or stories. Often defaults to around 0.7. (Microsoft Learn)
4. top_p (nucleus sampling)
Another way to manage randomness. Instead of looking at all tokens, the model samples from a dynamic subset representing the cumulative probability mass. Typically, you adjust either temperature or top_p, not both simultaneously. (Microsoft Learn)
Additional Frequently-Seen Parameters
Depending on the API and your use case, you might also encounter:
n: Number of responses to generate (e.g., return 2–5 alternatives).stop: Up to four strings where the model will cease generation if encountered.max_tokens: Caps the length of the generated response.stream: If set to true, results come token-by-token for real-time flows.user: A string to identify which user is making the request, often for tracking or usage analytics.
About GitHub Copilot “API”
GitHub Copilot currently doesn’t offer developers a direct API for code completions (like OpenAI’s endpoints) to control parameters like temperature, messages, etc. (Stack Overflow). So if you’re referring to “Copilot API,” you’re likely talking about the general pattern used by typical LLM “chat completion” endpoints—not something public for Copilot.
Collated Parameters (No Table, Just Bullets)
-
Required:
modelmessages
-
Common tuning knobs:
temperaturetop_p- (Use one at a time)
-
Additional controls:
nstopmax_tokensstreamuser
Would you like to dive deeper into how to tune these for specific purposes—like code generation, document summarization, or creative writing? Or want to see how they’re used in different platforms (e.g., OpenAI vs. Azure)?