Chat models have become the default interface for production LLM applications. Whether you are building a customer support bot, a coding assistant, or an autonomous research agent, the way you structure prompts, manage context, and select model capabilities determines reliability and cost. This guide covers practical patterns that improve output quality, with concrete examples you can run against any OpenAI-compatible endpoint.
Structuring System and User Messages
Well-structured conversations start with clear role separation. The system message sets behavior, constraints, and output format, while user messages supply context and queries. Avoid stuffing dynamic context into the system role. Instead, keep system instructions stable and place variable data in user messages or assistant turns. This reduces prompt injection risk and makes caching strategies more effective.
For tasks requiring strict formatting, instruct the model explicitly in the system prompt and set a low temperature. A temperature between 0.1 and 0.3 works well for classification, extraction, and tool routing. For creative drafting, a temperature closer to 0.7 produces more varied outputs.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a precise technical assistant. Answer in one paragraph."},
{"role": "user", "content": "Summarize the benefits of request-based pricing for long-context workloads."}
],
temperature=0.2
)Selecting the Right Model for the Workload
No single model is optimal for every task. Oxlo.ai offers more than 45 open-source and proprietary models across seven categories, fully accessible through a unified chat completions endpoint. For general-purpose reasoning, Llama 3.3 70B serves as a reliable flagship. When you need deep reasoning or complex coding, DeepSeek R1 671B MoE and Kimi K2.6 provide advanced chain-of-thought capabilities. For efficient agent workflows with extensive context, DeepSeek V4 Flash supports a 1 million token context window, and Kimi K2.6 handles vision and agentic coding across 131K tokens.
For coding-specific tasks, consider Qwen 3 Coder 30B, DeepSeek Coder, or Minimax M2.5. If you are prototyping under a free tier, DeepSeek V3.2 offers strong coding and reasoning capabilities at no cost. GLM 5, a 744B parameter MoE, targets long-horizon agentic tasks that require sustained planning.
Managing Context Windows and Costs
Long inputs are unavoidable in retrieval-augmented generation, document analysis, and multi-step agent loops. Under token-based pricing, every additional sentence in the prompt increases cost, which makes long-context workloads expensive to run at scale. Oxlo.ai uses request-based pricing, charging one flat cost per API call regardless of prompt length. For workloads that pass large documents or conversation history on every turn, this model can be significantly cheaper than token-based alternatives.
To maximize efficiency, trim obsolete turns from the conversation history and use summaries for earlier parts of a dialogue. When you do need the full history, a flat per-request structure removes the penalty for long inputs. See https://oxlo.ai/pricing for plan details.
Enforcing Structured Outputs and Tool Use
Production integrations rarely consume raw prose. Function calling and JSON mode let you route model outputs directly into your application logic. Oxlo.ai supports both features across its chat models, including multi-turn tool use and streaming responses.
When using function calling, describe each tool with a clear name and JSON schema. Set tool_choice: "auto" to let the model decide whether to call a function, or force it with tool_choice: {"type": "function", "function": {"name": "my_tool"}} for deterministic pipelines.
tools = [
{
"type": "function",
"function": {
"name": "
Top comments (0)