Optimizing LLM Models for High Accuracy: Best Practices and Techniques

Large language models deliver impressive zero-shot performance, but production accuracy depends on far more than base model capability. The gap between a demo and a reliable system is built through deliberate optimization across the inference stack, from prompt construction and retrieval context to sampling parameters and evaluation loops. For teams running high-stakes applications, the platform powering inference matters as much as the model weights.

Prompt Engineering and Structured Generation

Accuracy starts at the input. A well-structured prompt with a clear system message, explicit task definition, and few-shot examples often outperforms a larger model fed a vague query. Constraining output format is equally important. Forcing the model to emit valid JSON or adhere to a strict schema reduces hallucinations and simplifies downstream parsing.

Oxlo.ai supports JSON mode and function calling across its chat models, so you can enforce structure without post-processing regex. Because the platform is fully OpenAI SDK compatible, switching to Oxlo.ai requires only a base URL change.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant. Answer only in valid JSON."},
        {"role": "user", "content": "Extract the name, date, and amount from this invoice: ..."}
    ],
    response_format={"type": "json_object"},
    temperature=0.1
)

Low temperature and structured decoding together keep generations deterministic and parseable.

Retrieval-Augmented Generation and Context Management

Even the most capable models hallucinate when they lack domain-specific facts. RAG mitigates this by injecting retrieved documents into the prompt, but accuracy depends on chunk boundaries, embedding quality, and how much context you can afford to send.

Token-based billing penalizes long prompts, which creates pressure to trim context and sacrifice accuracy. Oxlo.ai uses flat per-request pricing, so cost does not scale with input length. This makes it practical to send full document pages or extended conversation histories without budget surprises. Models such as DeepSeek V4 Flash (1M context) and Kimi K2.6 (131K context) are ideal for workloads where large retrieval windows improve answer fidelity.

When building context, place the most critical instructions and retrieved passages near the beginning and end of the prompt. Interleave source metadata so the model can cite origins, which improves verifiability.

Model Selection and Architecture

No single model is optimal for every task. A 70B dense model may excel at general reasoning, while a 671B mixture-of-experts architecture delivers stronger coding accuracy with lower active parameter cost. The right choice depends on latency requirements, reasoning depth, and whether the workload benefits from long-horizon agentic planning.

Oxlo.ai hosts 45+ models across seven categories, including DeepSeek R1 671B MoE for complex coding and reasoning, Qwen 3 32B for multilingual agent workflows, and GLM 5 for long-horizon agentic tasks. Because the platform offers no cold starts on popular models, you can route requests to the best model for each subtask without latency penalties.

A routing layer can send simple summarization to a fast model like Oxlo.ai Coder Fast or DeepSeek V3.2, while reserving DeepSeek R1