Building a production question answering system with large language models requires more than prompting a chat endpoint. You need to manage retrieval, context assembly, reasoning, and cost control. For teams shipping document Q&A, support bots, or research assistants, the choice of inference backend directly impacts latency, scalability, and budget.
Architecture Patterns
Most LLM QA systems follow one of three patterns.
Retrieve-then-generate (RAG). Documents are chunked, embedded, and indexed. At query time, relevant chunks are retrieved and pasted into the prompt. This keeps individual requests small but adds retrieval infrastructure and can omit cross-chunk context.
Long-context stuffing. The entire source document, or a large section of it, is placed directly into the prompt. This removes retrieval complexity but demands a model with a large context window and an inference provider that does not penalize long inputs.
Agentic loops. The model uses tools, such as search or code execution, to gather information across multiple turns before synthesizing an answer. This is flexible for ambiguous questions but can generate many API calls.
Each pattern has tradeoffs, and many production systems blend all three. The constant across them is the cost of moving text through the LLM.
The Cost of Context
Token-based pricing penalizes long inputs. Every retrieved paragraph or uploaded PDF page adds to the bill. For a QA system processing technical manuals, legal contracts, or transcript archives, costs scale linearly with document size.
Oxlo.ai uses request-based pricing, charging one flat cost per API call regardless of prompt length. This means stuffing a 100K context window or running a multi-turn agentic chain does not inflate the per-request price. For long-context workloads, this structure can make Oxlo.ai significantly cheaper than token-based alternatives. To compare plans, see the Oxlo.ai pricing page.
Oxlo.ai also offers models with expansive context windows for exactly this use case. DeepSeek V4 Flash supports a 1M token context, and Kimi K2.6 handles 131K tokens with advanced reasoning and vision capabilities. If you prefer to stay in a smaller window, Llama 3.3 70B provides a strong general-purpose foundation.
Selecting a Model
Oxlo.ai offers 45+ open-source and proprietary models across 7 categories, all exposed through a single OpenAI-compatible endpoint. For QA pipelines, the right model depends on the complexity of the source material and the required output format.
- General knowledge and fast responses: Llama 3.3 70B or DeepSeek V3.2.
- Deep reasoning over dense text: DeepSeek R1 671B MoE or Kimi K2 Thinking.
- Multilingual or agentic workflows: Qwen 3 32B or GLM 5.
- Vision-enabled Q&A: Kimi K2.6 or Gemma 3 27B for questions that involve charts, diagrams, or scanned pages.
Because Oxlo.ai exposes all models through the same https://api.oxlo.ai/v1 base URL, switching from a fast baseline to a heavy reasoning model is a single parameter change.
Building the Pipeline
Below is a minimal Python example that sends a long technical document and a question to an LLM via the OpenAI SDK. The example uses JSON mode to enforce structured output, which is useful for downstream parsing or citation tracking.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
document = """
[Insert long technical manual, contract, or research paper here.
This could be tens of thousands of tokens without retrieval chunking.]
"""
question = "What is the root cause of the failure described in section 4?"
response = client.chat.completions.create(
model="deepseek-r1-671b",
messages=[
{
"role": "system",
"content": (
"You are a precise technical assistant. "
"Answer in JSON with keys: 'answer', 'confidence', 'citations'."
)
},
{
"role": "user",
"content": f"Document:\n{document}\n\nQuestion: {question}"
}
],
response_format={"type": "json_object"}
)
result = json.loads(response.choices[0].message.content)
print(json.dumps(result, indent=2))
This pattern works without code changes for any Oxlo.ai chat model, including those with 1M context windows. Because pricing is per request, you can pass the full document rather than spending engineering hours on aggressive chunking and re-ranking.
Tool Use and Agentic QA
For questions that span multiple documents or require calculation, function calling lets the model decide when to query a search index, calculator, or database. Oxlo.ai supports function calling and streaming across its chat models, so you can build agentic loops without custom inference code.
An agentic QA system might:
- Receive a user question.
- Call the model with available tools, such as web_search or calculate.
- Feed tool results back into the context.
- Return a synthesized answer.
With request-based pricing, each tool-call turn costs the same flat rate, making agentic behavior predictable even when the conversation grows.
Deployment Considerations
Latency and availability matter in production. Oxlo.ai loads popular models with no cold starts, so the first request after a quiet period returns at full speed. If you need guaranteed throughput and dedicated resources, the Enterprise tier provides custom arrangements and dedicated GPUs.
Evaluating Your System
Cost structure should not be the only metric. Measure answer correctness with a labeled evaluation set, track retrieval precision for RAG pipelines, and monitor JSON schema adherence if you rely on structured outputs. Oxlo.ai’s JSON mode and multi-turn conversation support make it straightforward to run repeatable evaluation scripts against any model in the catalog, from Qwen 3 to DeepSeek V4 Flash.
LLM-powered question answering sits at the intersection of retrieval, reasoning, and cost engineering. Oxlo.ai’s request-based pricing removes the penalty for long inputs, while its broad model catalog and OpenAI SDK compatibility let you iterate on architecture without migrating integrations.
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