Using LLM for Code Generation: Best Practices and Workflows

#aiinfrastructure #oxlo #ai

Code generation is one of the most widely deployed LLM workloads in production, but generating syntax that is correct, secure, and maintainable requires more than a well-worded prompt. The difference between a prototype and a reliable coding assistant comes down to model selection, prompt engineering, validation loops, and infrastructure that handles long context without unpredictable costs. This article walks through practical workflows that improve output quality, and shows how to run them on Oxlo.ai.

Pick the Right Model for the Task

Code tasks are not uniform. Deep architectural reasoning benefits from large reasoning models, while high-frequency autocomplete or linting demands low-latency specialized coders. Match the model to the complexity of the task:

Complex reasoning and system design: DeepSeek R1 671B MoE, Kimi K2.6, or GLM 5 handle long-horizon agentic tasks and deep chain-of-thought reasoning.
General-purpose generation: Llama 3.3 70B, DeepSeek V3.2, and Qwen 3 32B provide balanced performance across languages.
Specialized coding: Qwen 3 Coder 30B, DeepSeek Coder, and Oxlo.ai Coder Fast are optimized for syntax accuracy and faster turnaround.

Oxlo.ai hosts 45+ models across these categories, all exposed through a single OpenAI-compatible endpoint. You can switch from a lightweight coder to a heavy reasoning model without refactoring your client code.

Design Your Prompts for Deterministic Output

Stochastic creativity is useful for brainstorming, but a liability when generating boilerplate or API integrations. Lower the temperature, constrain the output format, and use system prompts to enforce style guides.

import openai

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

response = client.chat.completions.create(
    model="oxlo.ai-coder-fast",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a senior Python engineer. "
                "Respond with only valid Python code. "
                "Do not include explanations or markdown fences."
            )
        },
        {
            "role": "user",
            "content": (
                "Write a FastAPI endpoint that accepts a JSON payload, "
                "validates it with Pydantic, and returns a 201 status code."
            )
        }
    ],
    temperature=0.1,
    response_format={"type": "text"}
)
print(response.choices[0].message.content)

For structured generation, such as extracting function signatures or generating JSON config, use JSON mode to parse outputs reliably into your downstream pipeline.

Implement a Validation and Test Loop

Generated code should be treated as untrusted input. A robust workflow parses the output, runs static analysis, executes tests in a sandbox, and feeds errors back to the model for correction.

Parse: Strip markdown fences and extract runnable code.
Lint: Run ruff, eslint, or your language-specific linter to catch syntax and style issues.
Test: Execute unit tests in an isolated environment. Capture stdout and stderr.
Retry: If tests fail, append the traceback to the conversation context and ask the model to fix the code.

This loop prevents broken code from reaching your codebase, and it trains the model on your specific conventions through iterative feedback.

Manage Context Windows Intentionally

Long files, extensive libraries, and large diffs quickly consume context windows. Instead of dumping entire repositories into the prompt, retrieve only relevant snippets using embeddings or tree-sitter parsing. When you do need to send large blocks, for example during multi-file refactoring or codebase-wide migrations, infrastructure costs can spiral on token-based platforms.

Oxlo.ai uses flat per-request pricing, so the cost of a generation task does not scale with prompt length. A request that sends 4k tokens costs the same as one that sends 100k tokens. For refactoring workflows, agentic passes over large files, or few-shot examples with extensive context, this makes costs predictable and can be significantly cheaper than token-based alternatives such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale.

Build Agentic Workflows with Tool Use

Modern coding agents do not generate in a vacuum. They read files, run tests, search documentation, and commit changes. Function calling lets an LLM invoke external tools to ground its output in real project state.

Oxlo.ai supports function calling and tool use across its chat and reasoning models. A typical agent loop looks like this:

tools = [
    {
        "type": "function",
        "function": {
            "name": "run_tests",
            "description": "Run pytest and return the output",
            "parameters": {
                "type": "object",
                "properties": {
                    "path": {"type": "string"}
                },
                "required": ["path"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="deepseek-v3-2",
    messages=[{"role": "user", "content": "Refactor utils.py to use pathlib instead of os.path"}],
    tools=tools,
    tool_choice="auto"
)

# Execute any tool calls, append results, and call the model again

By giving the model access to live test results and file contents, you reduce hallucinated imports and outdated APIs.

Monitor Cost and Latency at Scale

As usage grows, small inefficiencies compound. Track which prompts produce the most retries, which models have the lowest latency for your language, and how context size affects time-to-first-token. Because Oxlo.ai charges per request rather than per token, your unit economics are straightforward: long system prompts, detailed few-shot examples, and verbose error traces do not inflate the price of individual calls.

If you are migrating from a token-based provider, you can compare your current spend against Oxlo.ai flat-rate request pricing. For teams with heavy coding workloads, the Enterprise plan offers dedicated GPUs and guaranteed savings. See https://oxlo.ai/pricing for details.

Putting It Into Practice

Reliable code generation is a systems problem. Start with a specialized model, constrain the output format, validate every generation, and close the loop with tool use. Run the workflow on infrastructure that does not penalize long context, so you can include the files, tests, and examples needed for high-quality output.

Oxlo.ai gives you access to specialized coders like Qwen 3 Coder 30B and Oxlo.ai Coder Fast, reasoning models like DeepSeek R1 671B MoE, and full OpenAI SDK compatibility, all with flat per-request pricing. You can start on the Free tier with 60 requests per day and 16+ free models, including DeepSeek V3.2, or explore the Pro and Premium plans for higher throughput and priority access.

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