LLM for Question Answering Tasks

Question answering is one of the most common production workloads for large language models. Whether you are building an internal knowledge base, a customer support bot, or a research assistant, the basic contract is the same: the model must ingest context, reason over it, and return an accurate, grounded answer. The difference between a prototype and a production-grade QA system usually comes down to two factors: context capacity and inference cost.

Patterns for LLM Question Answering

Most production QA systems fall into one of three architectures. The first is direct prompting, where the entire knowledge corpus, or a pre-selected chunk, is passed to the model inside the system or user prompt. The second is retrieval-augmented generation (RAG), where an embedding model retrieves relevant passages and the LLM synthesizes an answer. The third is agentic multi-hop reasoning, where the model iteratively calls tools, searches documents, and refines its answer across multiple turns. Each pattern increases the average input length per request, which makes pricing structure a critical design decision.

The Context Window and Cost Problem

Token-based providers scale cost linearly with prompt length. In a QA system, that means every extra paragraph of product documentation, every previous turn in the conversation history, and every retrieved chunk in a RAG pipeline directly increases the bill. For long-context documents or agentic loops, this unpredictability compounds. Oxlo.ai uses request-based pricing: one flat cost per API call regardless of how many tokens are in the prompt. For QA workloads that stuff long documents or maintain multi-turn context, this can be 10x to 100x cheaper than token-based billing. You can explore the exact structure at https://oxlo.ai/pricing.

Implementing QA with Oxlo.ai

Because Oxlo.ai is fully OpenAI SDK-compatible, migrating an existing QA pipeline requires only a base URL change. Below is a minimal example in Python.

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 precise technical assistant. Answer using only the provided context."
        },
        {
            "role": "user",
            "content": "What is the maximum context length supported by DeepSeek V4 Flash?"
        }
    ]
)

print(response.choices[0].message.content)

The endpoint supports streaming, JSON mode, and function calling, so you can upgrade this snippet to a structured RAG backend or an agentic loop without changing the client setup.

Long-Context RAG without Token Surprises

When your source material exceeds a few thousand tokens, RAG systems often retrieve multiple chunks to ensure coverage. On token-based platforms, feeding five chunks of 4,000 tokens each plus a system prompt and conversation history creates a large input bill before the model generates a single answer character. On Oxlo.ai, the cost remains flat per request, so you can experiment with larger retrieval windows or even pass full documents directly to models with extended context windows.

document = """..."""  # long document text loaded here

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[
        {
            "role": "system",
            "content": "Answer the user's question using only the document below.\n\n" + document
        },
        {
            "role": "user",
            "content": "What are the key differences between MoE and dense transformer architectures?"
        }
    ]
)

With a 1 million token context window, DeepSeek V4 Flash on Oxlo.ai can ingest entire codebases, legal briefs, or research papers in a single request. Because Oxlo.ai does not charge by the token, the cost of that request is the same whether the document is 10,000 tokens or 900,000 tokens.

Selecting a Model for Your QA Pipeline

Oxlo.ai hosts more than 45 models across seven categories, giving you specific options for different QA requirements.

• General-purpose QA: Llama 3.3 70B is a reliable default for balanced reasoning and latency.
• Complex reasoning and coding: DeepSeek R1 671B MoE and Kimi K2.6 excel at chain-of-thought reasoning and agentic coding tasks, with Kimi K2.6 offering a 131K context window and vision support for multimodal QA.
• Long-document QA: DeepSeek V4 Flash provides the 1M context window for full-document ingestion without retrieval overhead.
• Multilingual QA: Qwen 3 32B handles multilingual reasoning and agent workflows.
• Cost-sensitive or high-volume: DeepSeek V3.2 is available on the free tier and works well for coding and reasoning QA at low cost.

All of these models are served with no cold starts, so latency remains consistent even for sporadic traffic.

Conclusion

Building a QA system that is both accurate and economical requires control over context and cost. Oxlo.ai removes the input-length penalty, letting you pass full documents, wide RAG windows, and long conversation histories without watching token meters spin. If you are currently using a token-based provider such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, the flat per-request model on Oxlo.ai is a direct, drop-in alternative. Start with the free tier to benchmark your existing prompts, then scale on a plan that matches your daily request volume.