Long-Form Generation with Large Language Models: How to Keep Structure, Coherence, and Facts Accurate

When you ask a large language model to write a 5,000-word report, a detailed white paper, or a multi-chapter story, it doesn’t just spit out words. It builds a structure-chapter by chapter, paragraph by paragraph. But here’s the problem: the longer the output, the more likely it is to drift off track, repeat itself, or invent facts that never happened. That’s not a bug. It’s a fundamental challenge in long-form generation with large language models.

How LLMs Build Long Texts

Large language models like GPT-4, Gemini 1.5, and Claude 3 don’t write like humans. They don’t start with an outline. They don’t plan ahead. Instead, they predict the next word, then the next, and the next-over and over-using patterns learned from hundreds of billions of text samples. This is called autoregressive generation.

What makes this work for long-form content is the model’s context window. Think of it as the model’s short-term memory. In 2024, models like Google’s Gemini 1.5 can hold up to 1 million tokens in memory. That’s roughly 750,000 words. For comparison, a typical novel is 80,000 words. So technically, a model can process an entire book in one go.

But here’s the catch: even with a huge context window, the model doesn’t understand the structure. It doesn’t know what a thesis statement is, or how a conclusion should tie back to an introduction. It only knows that certain phrases tend to follow others. If you prompt it with “Write a report on renewable energy trends,” it will generate something that looks right-until you notice that Section 3 suddenly claims solar panels were invented in 1982, when they were actually developed in the 1880s.

Why Coherence Falls Apart

Coherence isn’t just about grammar. It’s about logical flow. It’s about keeping the same tone, the same key terms, and the same direction over dozens of paragraphs. When a model generates text in chunks-say, 500 words at a time-it often forgets what came before.

Imagine writing a 10-page essay and forgetting halfway through that you already mentioned climate policy in Section 2. You start repeating yourself. That’s exactly what LLMs do. They don’t have a mental map of the whole document. They only see the last few hundred words.

Studies from Stanford and MIT in 2025 show that coherence drops sharply after 2,000 words. In tests where models were asked to write 3,000-word summaries of scientific papers, over 60% of outputs contained contradictions between early and late sections. One model described a study as “conclusive” in paragraph 5, then called it “inconclusive” in paragraph 27. No human would make that mistake. But for an LLM, it’s normal.

Structure Without Planning

Humans use outlines. LLMs don’t. But you can trick them into building structure anyway.

Here’s what works in practice:

• Ask the model to generate a detailed outline first. Don’t just say “write a report.” Say “Generate a 7-point outline for a report on AI in healthcare, with subpoints for each section.”
• Then, ask it to expand each point one at a time. This forces the model to focus on one chunk at a time.
• Use consistent prompts. If you start with “In this section, we discuss…” keep using that phrase. It helps the model recognize patterns.
• Insert markers like “Section 1: Background,” “Section 2: Data Analysis,” etc. These act as anchors.

Some advanced tools, like Anthropic’s Claude 3, now include a “structured generation” mode that automatically enforces headings and subheadings. But even then, you still need to review.

The Fact-Checking Problem

LLMs are trained on data that includes misinformation, outdated claims, and outright fabrications. They don’t know what’s true. They only know what’s common.

Take this example: In 2024, a major news outlet used a large language model to draft a 4,000-word feature on quantum computing. The article claimed that “China launched the world’s first quantum satellite in 2021.” It didn’t. That happened in 2016. The model had seen multiple articles with incorrect dates and averaged them out.

This is called hallucination. It’s not a glitch. It’s how these models work. They’re probability engines, not truth detectors.

So how do you fix it?

• Use retrieval-augmented generation (RAG). This means feeding the model real-time, verified sources-like peer-reviewed papers, government reports, or trusted databases-alongside your prompt.
• Run outputs through fact-checking tools. Tools like FactCheck.org’s AI checker, or proprietary systems from Google and OpenAI, can flag claims that don’t match known data.
• Require citations. If you ask the model to “cite sources,” it will often invent fake ones. But if you say “cite only from IEEE journals published between 2020 and 2025,” you limit its options.
• Compare outputs. Generate the same report three times. If all three say the same false thing, it’s likely a systemic error.

One company in Silicon Valley, using a custom pipeline, reduced factual errors in long-form reports by 72% by combining RAG with human-in-the-loop validation. They didn’t eliminate errors. They just made them rare enough to catch before publication.

What Works in Real-World Use

Businesses aren’t just testing this-they’re using it. A legal firm in London now uses LLMs to draft contracts and policy memos up to 15 pages long. Their process?

1. Start with a template based on past approved documents.
2. Feed the model the client’s data and relevant case law from their internal database.
3. Generate the draft in 5 sections, reviewing each before moving on.
4. Run the final text through a legal fact-checking tool that cross-references statutes and rulings.
5. Have a junior lawyer sign off before sending it to the client.

They cut drafting time by 60%. But they still have a human read every document. Not because the AI is bad. Because the AI doesn’t know what’s at stake.

Another example: a tech startup used LLMs to write product documentation for a new AI-powered analytics tool. The first draft had 14 incorrect technical specs. The second draft, after using RAG with their own API docs and engineering notes, had 2. They fixed those two manually.

The Bottom Line

Long-form generation with large language models isn’t magic. It’s a tool. And like any tool, it needs a skilled user.

You can’t just say “write me a white paper” and expect perfection. You need to:

• Build structure manually-guide the model with outlines and markers.
• Control coherence by generating in chunks and reviewing each part.
• Anchor facts in real data-use RAG, not just prompts.
• Always have a human check the final output.

The best long-form content today isn’t written by AI. It’s written by people who know how to use AI.