Building Academic Writing Tools with LLMs: A Step-by-Step Guide

Academic writing assistants need to handle long drafts, dense citations, and strict formatting without ballooning costs. In this guide, I will walk you through building a Python pipeline that ingests a raw draft, tightens the prose, and standardizes citations using an LLM hosted on Oxlo.ai. Because Oxlo.ai charges a flat rate per request rather than per token (see https://oxlo.ai/pricing), and because the API is a drop-in replacement for the OpenAI SDK, you can pass in entire sections or multi-page manuscripts with no cold starts and no refactoring.

What you'll need

• Python 3.10 or newer
• An Oxlo.ai API key from https://portal.oxlo.ai
• The OpenAI SDK: pip install openai
• A sample draft to test with (we will provide one below)

Step 1: Bootstrap the Oxlo.ai client

I start every project by verifying the endpoint. Oxlo.ai exposes a fully OpenAI-compatible API at https://api.oxlo.ai/v1, so the only change from a standard OpenAI script is the base URL and key. I use llama-3.3-70b here because it gives solid general-purpose reasoning for editorial tasks, but you can swap in kimi-k2.6 or deepseek-v3.2 without changing any other code.

from openai import OpenAI

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

# Verify connectivity with a short ping
response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Say 'Endpoint ready' and nothing else."}],
    temperature=0.0
)
print(response.choices[0].message.content)

Step 2: Define the academic editor prompt

A good system prompt keeps the model in a narrow lane. I want it to improve clarity, tighten wordy phrases, and maintain an objective academic tone, but never invent facts or change the underlying meaning. I store this as a module-level constant so I can version it easily.

SYSTEM_PROMPT = """You are an academic writing assistant. Your job is to improve the clarity, flow, and tone of scholarly text.

Rules:
- Preserve all factual claims, data, and citations exactly as given.
- Remove filler words, passive voice, and vague qualifiers.
- Ensure each paragraph has a clear topic sentence.
- Do not add new references or hallucinate statistics.
- Output only the revised text, with no meta-commentary.

Format: Return plain text with paragraph breaks. Use standard academic English."""

Step 3: Build the section improver

Most papers are broken into logical sections: Abstract, Introduction, Methods, Results, Discussion. I treat each section as an independent request so that context stays focused and I can parallelize later if needed. The function takes a section header and its body, wraps it in a user message, and returns the polished text.

def improve_section(header: str, body: str) -> str:
    user_message = f"SECTION: {header}\n\nDRAFT:\n{body}"
    
    response = client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": user_message},
        ],
        temperature=0.3,
    )
    return response.choices[0].message.content.strip()

Step 4: Add a citation formatter

Citation formatting is too rigid for a general prompt. I use a dedicated helper that targets in-text citations. It scans for messy parentheticals, et al. usage, and missing parentheses, then normalizes them to APA 7th edition. Keeping this separate from the prose editor prevents the model from overreaching and rewriting sentences while it fixes brackets.

CITATION_PROMPT = """You are a citation formatter. Given a block of academic text, standardize all in-text citations to APA 7th edition.

Rules:
- Use ampersands inside parentheses, and the word 'and' outside.
- Ensure et al. has a period after 'al' and no extra punctuation.
- Do not alter the prose outside the citations.
- Output only the text with corrected citations."""

def format_citations(text: str) -> str:
    response = client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[
            {"role": "system", "content": CITATION_PROMPT},
            {"role": "user", "content": text},
        ],
        temperature=0.1,
    )
    return response.choices[0].message.content.strip()

Step 5: Assemble the draft processor

Now I chain the two helpers into a single pipeline. The processor splits a markdown-flavored draft on headers, routes each section through the improver, joins them back together, and runs the entire document through the citation formatter. I use a lightweight regex split so the script has no heavy dependencies.

import re

def process_draft(draft: str) -> str:
    # Split on markdown headers like ## Abstract
    sections = re.split(r'\n(?=##\s)', draft.strip())
    
    improved = []
    for section in sections:
        if not section.strip():
            continue
        lines = section.strip().splitlines()
        header = lines[0].replace("##", "").strip()
        body = "\n".join(lines[1:]).strip()
        
        polished = improve_section(header, body)
        improved.append(f"## {header}\n\n{polished}")
    
    full_text = "\n\n".join(improved)
    return format_citations(full_text)

Step 6: Test with a sample draft

I always keep a deliberately sloppy sample in the repo for regression testing. This one has passive voice, vague qualifiers, and malformed citations. Running it end-to-end proves the pipeline works before I point it at real manuscripts.

SAMPLE_DRAFT = """## Abstract

It is widely believed that climate change has a big impact on agriculture. Smith et al (2020) found that yields drop. This paper looks at the data and tries to figure out what might maybe happen in the future.

## Discussion

The results were analyzed by us, and it was shown that temperature is very important. Jones and Baker (2019) and Lee 2021 have noted similar things. We feel that more research should be done."""

if __name__ == "__main__":
    final = process_draft(SAMPLE_DRAFT)
    print(final)

Run it

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

export OXLO_API_KEY="your-key-here"
python academic_editor.py

Example output:

## Abstract

Climate change substantially affects agricultural productivity. Smith et al. (2020) found that yields decline under warming conditions. This paper analyzes existing data and projects future outcomes.

## Discussion

We analyzed the results and demonstrated that temperature drives yield variation. Jones and Baker (2019) and Lee (2021) noted similar patterns. Further research is warranted.

Wrap-up

From here, you can add a JSON mode schema to extract structured metadata from each section, or wire the script into a Git pre-commit hook that lints markdown drafts automatically. If you start passing in full manuscripts instead of small sections, switch to kimi-k2.6 on Oxlo.ai to take advantage of its 131K context window. If you need deeper reasoning for complex statistical arguments, deepseek-v3.2 or qwen-3-32b are available with the same single-line model swap. Because Oxlo.ai pricing is per request rather than per token, the cost stays flat even when you feed in an entire long-context document.