Optimizing LLM Performance for Question Answering Tasks

Question answering systems are only as reliable as the infrastructure serving them. Whether you are building a support bot over thousands of pages of documentation or a legal research assistant that reasons across full case files, the pipeline involves retrieval, context assembly, model inference, and output validation. Each stage introduces latency and cost, especially when input contexts grow. Optimizing QA performance requires more than selecting a large language model. It demands deliberate choices about embeddings, context windows, model routing, and pricing structures that scale with your data, not just your token count.

Retrieval and Context Engineering

High-quality answers start before the LLM sees a prompt. A retrieval layer that returns irrelevant chunks forces the model to either hallucinate or abstain, wasting compute on a doomed context.

Use a hybrid retrieval stack. Dense retrieval with embedding models captures semantic meaning, while keyword search handles exact terminology. Oxlo.ai offers embedding endpoints for models such as BGE-Large and E5-Large, which you can use to vectorize documents and query them via your vector database.

After retrieval, rerank. A cross-encoder or pointwise reranker reorders the top-k chunks so the most relevant passages sit at the start of the context, where many decoder-only models attend most strongly.

Chunk sizing also matters. Overlapping chunks of 256 to 512 tokens usually strike a balance between specificity and continuity. If your documents contain tables or code, consider structure-aware splitting rather than fixed token boundaries.

import openai

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

def embed_chunks(chunks):
    response = client.embeddings.create(
        model="bge-large",  # verify exact ID in Oxlo.ai docs
        input=chunks
    )
    return [item.embedding for item in response.data]

Context Window Strategy

Once you have retrieved the best chunks, you must decide how much context to feed the model. Short-context RAG minimizes input length but risks losing inter-chunk relationships. Full-context stuffing, where you include an entire long document, preserves narrative structure and eliminates retrieval errors.

The obstacle to full-context stuffing is usually cost. On token-based providers, stuffing a 100,000-token legal brief into the prompt multiplies the per-request price. Oxlo.ai uses flat per-request pricing, meaning the cost does not scale with input length. This makes long-context QA economically viable for workloads that need entire documents, not just snippets.

If you choose full-context stuffing, select a model with an adequate context window. On Oxlo.ai, options include Kimi K2.6 with 131K context and DeepSeek V4 Flash with 1M context. Both support long-document comprehension without the price escalation typical of token-based billing.

Model Selection and Routing

Not every question requires a 671B parameter reasoning model. A simple lookup over an internal wiki can be handled by a fast, general-purpose model, while a multi-hop analytical question may need deep chain-of-thought reasoning.

Implement a lightweight router that classifies the query complexity before sending it to the LLM. Oxlo.ai hosts more than 45 models across seven categories, so you can match the model to the task without maintaining multiple provider integrations.

• Factual extraction: Llama 3.3 70B or Qwen 3 32B
• Complex reasoning and coding: DeepSeek R1 671B MoE or Kimi K2 Thinking
• Agentic workflows with tool use: GLM 5 or Minimax M2.5

Because Oxlo.ai is fully OpenAI SDK compatible, switching models is a single parameter change.

def route_and_answer(query, context):
    complexity = classify_complexity(query)  # your heuristic or classifier

    if complexity == "high":
        model = "deepseek-r1-671b"  # verify exact ID in Oxlo.ai docs
    else:
        model = "llama-3.3-70b"

    return client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "Answer based only on the provided context."},
            {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
        ],
        stream=True
    )

Structured Generation and Tool Use

Unstructured text outputs complicate downstream processing. For QA systems, forcing the model to return JSON with explicit fields for answer, confidence, and source citation eliminates regex parsing and reduces failure modes.

Oxlo.ai supports JSON mode and function calling through the standard chat/completions endpoint. You can define a JSON schema or supply tool definitions so the model emits structured data ready for your application logic.

response = client.chat.completions.create(
    model="qwen-3-32b",  # verify exact ID in Oxlo.ai docs
    messages=[{
        "role": "user",
        "content": (
            "Based on the context, provide the answer, confidence score from 0 to 1, "
            "and supporting quote. Return valid JSON."
        )
    }],
    response_format={"type": "json_object"}
)

structured = response.choices[0].message.content

If the question requires real-time data or calculation, use function calling. Define a tool for calculator lookup or database search, and let the model decide when to invoke it. This keeps the QA system grounded without hardcoding branching logic.

Latency and Streaming

Perceived latency matters in interactive QA. Waiting for the full response to generate before displaying it creates a sluggish experience. Streaming allows you to render tokens as they arrive, which improves perceived speed even if total generation time is unchanged.

Oxlo.ai supports streaming responses with no cold starts on popular models, so time-to-first-token remains consistent. Enable streaming by setting stream=True in your request.

stream = client.chat.completions.create(
    model="kimi-k2.6",  # verify exact ID in Oxlo.ai docs
    messages=[{"role": "user", "content": "Summarize the key holding in the attached opinion."}],
    stream=True
)

for chunk in stream:
    token = chunk.choices[0].delta.content
    if token:
        print(token, end="")

Where possible, parallelize retrieval and preprocessing. While the user types, prefetch related embeddings or warm up the context. Once the user submits, the inference request itself becomes the bottleneck, not the data pipeline.

Cost Control for Production QA

Production QA workloads often involve repetitive queries over large, static knowledge bases. Under token-based pricing, even a slight increase in context length, multiplied across thousands of daily requests, produces significant cost growth.

Oxlo.ai’s request-based pricing charges one flat cost per API call regardless of prompt length. For long-context and agentic QA workloads, this can be 10 to 100 times cheaper than token-based alternatives. You can stuff full documents, include few-shot examples, or attach long conversational histories without watching the meter run on input tokens.

For prototyping, Oxlo.ai offers a free tier with 60 requests per day across more than 16 models, including options like DeepSeek V3.2. When you move to production, the Pro and Premium plans provide predictable daily request quotas. See exact rates at https://oxlo.ai/pricing.

Putting It Together

An optimized QA pipeline combines dense retrieval, smart context assembly, model routing, structured output, and streaming. The infrastructure underneath must support these patterns without imposing hidden costs or cold-start penalties.

Oxlo.ai provides the models, the OpenAI-compatible endpoints, and the flat per-request pricing that makes long-context QA practical at scale. Whether you are serving fast factual lookups or deep reasoning over million-token documents, you can tune the pipeline for accuracy and latency while keeping costs predictable.