Domain Adaptation for LLMs: Oxlo's Approach

#aiinfrastructure #oxlo #ai

Domain adaptation is usually framed as a training problem, but in production it is mostly an inference problem. Whether you are grounding a model with retrieval chunks, few-shot examples, or detailed system prompts, the actual work happens at inference time. The bottleneck is rarely the base model. It is the cost and latency of feeding that model enough context to behave like a domain expert. This is where the inference platform matters as much as the model itself.

The Inference Cost of Context

Adapting a general model to a specialized domain without fine-tuning means increasing prompt size. Legal analysis, medical coding, and technical support all require extensive system prompts, document snippets, or conversation history. On token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, longer inputs directly inflate costs. If you retrieve ten document chunks for RAG, you pay for every token in every chunk, even if the user asked a single question. Oxlo.ai uses request-based pricing, so the cost stays flat regardless of how much domain context you pack into the prompt. For long-context workloads, this structural difference can make domain adaptation economically viable at scale. See the exact rates at https://oxlo.ai/pricing.

In-Context Learning as Adaptation

Fine-tuning is not always necessary. A strong base model with a large context window can adapt to a new domain through carefully constructed prompts. Oxlo.ai hosts models like DeepSeek V4 Flash with a 1 million token context window and Kimi K2.6 with 131K context, both suited for massive few-shot prompts or full document analysis. Because Oxlo.ai does not charge per token, you can include hundreds of examples or entire manuals without worrying about input costs.

import openai
import os

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ["OXLO_API_KEY"]
)

# DeepSeek V4 Flash on Oxlo.ai supports up to 1M tokens of context
response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {"role": "system", "content": "You are a clinical coding assistant."},
        {"role": "user", "content": f"Code the following encounter according to ICD-10 guidelines:\n\n{clinical_notes}"}
    ],
    temperature=0.2
)

RAG Without Token Anxiety

Retrieval Augmented Generation is the most common form of domain adaptation. The challenge is deciding how many chunks to retrieve. With token-based billing, each extra chunk adds cost. On Oxlo.ai, the request price is fixed, so you can retrieve generously, re-rank, and stuff the full context window to maximize recall. Models like Llama 3.3 70B and Qwen 3 32B handle multi-document reasoning well. You trade latency for accuracy, not money.

context = "\n\n---\n\n".join(retrieved_chunks)  # Pack all retrieved chunks

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "Answer using only the provided documents."},
        {"role": "user", "content": f"Documents:\n{context}\n\nQuestion: {user_query}"}
    ]
)

Structured Outputs and Tool Use

Domain adaptation often requires more than text generation. It requires extracting entities, calling internal APIs, or returning JSON schemas. Oxlo.ai supports JSON mode and function calling across its catalog, so you can force a model to output valid domain-specific structures. This turns an LLM into a reliable component inside a larger pipeline.

response = client.chat.completions.create(
    model="kimi-k2-6",
    messages=[
        {"role": "system", "content": "Extract entities as JSON."},
        {"role": "user", "content": domain_text}
    ],
    response_format={"type": "json_object"}
)

Choosing the Right Backbone on Oxlo.ai

Not every domain needs the same model. Oxlo.ai offers 45+ models across categories. For coding domains, DeepSeek V3.2 or Qwen 3 Coder 30B are solid choices. For deep reasoning, DeepSeek R1 671B MoE or Kimi K2 Thinking provide chain-of-thought capabilities. For multilingual domains, Qwen 3 32B is explicitly designed for cross-lingual tasks. For agentic workflows that need tool use, GLM 5 or Minimax M2.5 are available. All are accessible through the same OpenAI-compatible endpoint with no cold starts.

Conclusion

Domain adaptation does not have to mean fine-tuning pipelines and labeled datasets. Often, it is about giving the right model the right context at the right price. Oxlo.ai's request-based pricing removes the tax on long prompts, making in-context learning and large-scale RAG practical for production. With broad model support, full OpenAI SDK compatibility, and no cold starts, you can treat inference as the adaptation layer. Explore the flat pricing at https://oxlo.ai/pricing.

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