shashank ms

Posted on Jul 11

Best Practices for LLM-Assisted Academic Writing

#learnai #oxlo #ai

We are building a command-line academic writing assistant that turns a rough research idea into a structured outline and full sections. It runs entirely on Oxlo.ai, so the flat per-request pricing keeps long brainstorming sessions affordable compared to token-based providers. Graduate students and researchers can use it to iterate on paper structure without watching API costs scale with every paragraph.

What you'll need

Python 3.10 or newer
The OpenAI SDK: pip install openai
An Oxlo.ai API key from https://portal.oxlo.ai

Step 1: Configure the client

First, I set up the OpenAI-compatible client pointing at Oxlo.ai and make a quick health check call to confirm the key works.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ.get("OXLO_API_KEY")
)

# Quick connectivity test
response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Say OK"}],
    max_tokens=10,
)
print(response.choices[0].message.content)

Step 2: Define the system prompt

The system prompt anchors the model in academic conventions. I keep it in a separate constant so I can tune voice and citation rules without touching business logic.

SYSTEM_PROMPT = """You are an academic writing assistant. Your goals are:
- Produce clear, structured scholarly prose.
- Use Markdown headings for organization.
- Insert [CITATION] placeholders where empirical claims need support.
- Flag uncertain facts with [VERIFY].
- Avoid hype, jargon, or unsupported superlatives.
- Write in a neutral, precise tone suitable for journals or conference papers."""

Step 3: Generate a structured outline

I send the research topic to the model and ask for a hierarchical outline with word-count budgets per section. I use Qwen 3 32B because it handles multilingual reasoning and structured agent workflows well.

def generate_outline(topic, target_words=3000):
    prompt = f"""Topic: {topic}
Target length: {target_words} words.
Return a hierarchical outline with:
1. Title
2. Abstract summary (3 sentences)
3. Sections (Introduction, Related Work, Method, Results, Discussion, Conclusion)
4. Approximate word count for each section
Use Markdown. Do not write prose yet."""
    
    resp = client.chat.completions.create(
        model="qwen-3-32b",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        temperature=0.3,
    )
    return resp.choices[0].message.content

# Example usage
outline = generate_outline(
    "The impact of retrieval-augmented generation on citation accuracy in scholarly LLMs",
    target_words=4000,
)
print(outline)

Step 4: Draft sections with citation placeholders

Next, I take one section heading and expand it into full prose. I pass the entire outline as context so the model stays coherent with the overall narrative arc. DeepSeek V3.2 is strong at coding and reasoning, so I use it here for tight logical structure.

def draft_section(section_heading, outline, notes=""):
    prompt = f"""Given the following outline:
{outline}

Expand only the section titled '{section_heading}' into full scholarly prose.
- Follow the word budget implied by the outline.
- Insert [CITATION] wherever empirical data or prior work is referenced.
- Flag uncertain claims with [VERIFY].
- Use Markdown subheadings if the section is long.

Additional researcher notes: {notes if notes else "None"}"""
    
    resp = client.chat.completions.create(
        model="deepseek-v3.2",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        temperature=0.4,
        max_tokens=2048,
    )
    return resp.choices[0].message.content

# Draft the Introduction
introduction = draft_section(
    "Introduction",
    outline,
    notes="Emphasize the reproducibility crisis in NLP benchmarks.",
)
print(introduction)

Step 5: Add a critique and refinement loop

Raw drafts usually need tightening. I send the section back to the model with an explicit critique instruction. Kimi K2.6 handles advanced chain-of-thought reasoning, so it is a good fit for spotting logical gaps.

def critique_and_refine(section_text, section_heading):
    critique_prompt = f"""Section: {section_heading}

Draft:
{section_text}

First, list 3 concrete weaknesses (logic, clarity, or missing citations).
Then, rewrite the section fixing those weaknesses.
Preserve all [CITATION] and [VERIFY] tags, and add new ones where needed."""
    
    resp = client.chat.completions.create(
        model="kimi-k2.6",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": critique_prompt},
        ],
        temperature=0.3,
    )
    return resp.choices[0].message.content

refined_intro = critique_and_refine(introduction, "Introduction")
print(refined_intro)

Step 6: Wire everything into a single script

Finally, I wrap the pipeline in a small CLI that accepts a topic and runs the full flow. Because Oxlo.ai charges a flat rate per request, I know the cost of this multi-step agentic workflow upfront without counting tokens.

import argparse

def main():
    parser = argparse.ArgumentParser(description="Academic writing assistant")
    parser.add_argument("topic", help="Research topic")
    parser.add_argument("--words", type=int, default=3000, help="Target word count")
    parser.add_argument("--sections", nargs="+", default=["Introduction", "Method", "Conclusion"])
    args = parser.parse_args()

    print("=== GENERATING OUTLINE ===")
    outline = generate_outline(args.topic, args.words)
    print(outline)
    print("\n")

    for sec in args.sections:
        print(f"=== DRAFTING: {sec} ===")
        draft = draft_section(sec, outline)
        print(draft)
        print("\n")

        print(f"=== REFINING: {sec} ===")
        refined = critique_and_refine(draft, sec)
        print(refined)
        print("\n")

if __name__ == "__main__":
    main()

Run it

Save the script as academic_writer.py, export your key, and run:

export OXLO_API_KEY="sk-oxlo.ai-..."
python academic_writer.py "Federated learning for low-resource language modeling" --words 3500 --sections Introduction Method Results

Typical output looks like this:

=== GENERATING OUTLINE ===
# Federated Learning for Low-Resource Language Modeling

## Abstract
Federated learning (FL) offers a privacy-preserving alternative to centralized training, yet its effectiveness for low-resource languages remains underexplored. [CITATION] ...

## Outline
- Introduction (800 words)
  - Motivation: data scarcity and privacy ...
- Method (1200 words)
  ...
=== DRAFTING: Introduction ===
## Introduction

Low-resource languages, defined as those with fewer than one million monolingual sentences in publicly available corpora, [CITATION] present a unique challenge for modern NLP. Federated learning provides a distributed training paradigm ... [VERIFY]
...

Wrap-up

You now have a working academic writing pipeline on Oxlo.ai. Two concrete next steps: wire the [CITATION] placeholders to a retrieval layer using Oxlo.ai's embeddings endpoint with bge-large, or add a --latex flag that converts the Markdown output to .tex sections for direct integration with your manuscript repository.

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