Unlocking the Power of LLM for Question Answering

#aiinfrastructure #oxlo #ai

Large language models have become the default backend for modern question answering systems. Whether you are building an internal knowledge base, a customer support bot, or a research assistant, the core challenge remains the same: delivering accurate, grounded answers while controlling latency and cost. This article breaks down the architectural patterns that make LLM-powered QA reliable, and how to implement them using inference platforms designed for production workloads.

LLM Question Answering Fundamentals

There are two primary approaches to LLM question answering. The first is direct prompting, where the model relies entirely on parametric knowledge encoded during training. The second is retrieval-augmented generation (RAG), where the system retrieves relevant documents or snippets and presents them as context before generating an answer.

Direct prompting works well for general knowledge, but it hallucinates on proprietary or recent data. RAG grounds the model in external evidence, which reduces hallucination and lets you update answers without retraining. Most production systems use some form of RAG, often paired with re-ranking and hybrid search.

The Cost of Context in Document QA

QA over long documents, legal contracts, or technical manuals requires feeding substantial context into the prompt. Under token-based pricing, longer inputs mean higher costs per request, and multi-turn conversations only amplify the problem. This creates a tension between answer quality (more context) and budget predictability.

Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. Unlike token-based providers, cost does not scale with input length, so Oxlo.ai is significantly cheaper for long-context and agentic workloads. For QA systems that process entire documents or maintain long conversation histories, this model removes the penalty for context richness. You can explore the structure at https://oxlo.ai/pricing.

Building a RAG Pipeline with Oxlo.ai

A complete RAG pipeline needs two inference stages: embedding retrieval and answer generation. Oxlo.ai supports both via fully OpenAI-compatible endpoints. The platform offers embedding models such as BGE-Large and E5-Large, and generation models ranging from Llama 3.3 70B to DeepSeek R1 671B MoE.

import openai

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

# 1. Embed the query
query = "What are the termination clauses in the service agreement?"
query_emb = client.embeddings.create(
    model="bge-large",
    input=query
).data[0].embedding

# 2. Retrieve relevant chunks (pseudo-code for your vector DB)
# chunks = vector_db.search(query_emb, top_k=5)

context = "\n\n".join(chunks)

# 3. Generate answer
response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "Answer based only on the provided context."},
        {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
    ],
    stream=False
)

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

Because Oxlo.ai is fully OpenAI SDK compatible, you can drop this into existing Python, Node.js, or cURL workflows without rewriting your client logic. There are no cold starts on popular models, so the first request after idle time returns at full speed.

When to Use Long-Context Instead of RAG

RAG adds infrastructure complexity: chunking strategies, embedding pipelines, and re-rankers. For many use cases, passing the full document directly into the prompt is simpler and often more accurate, provided your model has a large enough context window.

Oxlo.ai hosts models that support extensive context lengths. DeepSeek V4 Flash offers a 1M context window with efficient MoE architecture, making it suitable for processing entire codebases or books in a single request. Kimi K2.6 provides 131K context with advanced reasoning and vision capabilities, which is ideal for document QA that mixes text and figures. Because Oxlo.ai charges per request rather than per token, stuffing a 100K-token document costs the same as a one-line prompt. This can be 10-100x cheaper than token-based billing for long-context workloads.

Multi-Turn Conversations and Memory

Conversational QA systems rarely answer in isolation. Users ask follow-ups, refine constraints, and reference prior turns. Each additional turn increases the total token count sent to the model.

With request-based pricing, the cost per turn stays flat even as the conversation history grows. Oxlo.ai supports multi-turn conversations, streaming responses, and JSON mode out of the box. You can stream partial answers to keep the interface responsive, then enforce structured output for downstream processing.

Selecting the Right Model on Oxlo.ai

Oxlo.ai offers 45+ open-source and proprietary models across 7 categories. For QA workloads, the following mappings work well:

General knowledge and fast answers: Llama 3.3 70B
Deep reasoning and complex coding questions: DeepSeek R1 671B MoE or Kimi K2.6
Multilingual document QA: Qwen 3 32B
Ultra-long documents: DeepSeek V4 Flash (1M context)
Vision-enabled QA: Kimi VL A3B or Gemma 3 27B

All of these are accessible through the same chat/completions endpoint with no routing logic required.

Production Deployment Tips

Moving from prototype to production requires more than a correct answer. Consider these practical constraints:

Structured output: Use JSON mode to constrain answers into schemas you can validate programmatically.
Tool use: For QA systems that need to query APIs, calculators, or databases, use function calling to let the model delegate precise operations.
Streaming: Enable streaming responses to improve perceived latency in user interfaces.
Evaluation: Maintain a golden dataset of question-answer pairs and measure factual consistency with an LLM judge or NLI model.

Oxlo.ai supports streaming, function calling, JSON mode, and vision input on compatible models, so you can implement these patterns without switching providers.

Getting Started with Oxlo.ai

You can prototype a QA system on Oxlo.ai without upfront cost. The Free plan includes 60 requests per day across 16+ free models, and new accounts receive a 7-day full-access trial. For production traffic, the Pro plan offers 1,000 requests per day across all models, while Premium provides 5,000 requests per day with priority queue access. Enterprise plans include dedicated GPUs and a guaranteed 30% savings versus your current provider.

To start, point your OpenAI client to https://api.oxlo.ai/v1, select a model that matches your context and reasoning requirements, and build. For detailed plan breakdowns, visit https://oxlo.ai/pricing.