Write for Us LLMs: LLMs Blog Guest Post Guidelines

Yes, large language models guest posts are accepted on this blog. The ideal writer is a builder, product lead or founder who has shipped something on top of a large language model in a real product. We decline explainers of how large language models work, and posts written to sell a course, an API wrapper or a platform.

Plenty of posts explain what a large language model is. Very few show what it took to build something dependable on one. What this blog wants is a real build on an LLM, the use case, how you handled prompts, context, cost and the times it hallucinated, and what you learned about making it reliable enough to ship.

My readers are founders, product people and technical marketers weighing whether to build on an LLM. They want the honest engineering account: what worked, what it cost, and how you stopped it embarrassing you in front of a user, so an LLM post has to teach from a shipped build, not a concept.

60-second large language models guest post check

A strong submission can answer yes to all five points:

Have you shipped the LLM build you are describing
Do you have costs, trade-offs or a reliability result to share
Does your opening answer the reader's question straight away
Have you removed any pitch for a model, wrapper or platform
Could a builder learn from your engineering decisions

Read the guidelines and submit your guest post.

What we publish

A feature you shipped on an LLM and how you made it reliable
Handling hallucinations in production and the checks that caught them
Prompt and context design that changed your output quality
The real cost of running an LLM feature and how you controlled it
Choosing between models for a specific job, with the trade-offs
Retrieval or tools added to an LLM and what they fixed
An LLM build you scrapped and the reason
Evaluating LLM output quality without a human reading everything

If your post could be published on any large language models site with the logo swapped, it is not for us.

What we don't accept

Explainers of how large language models work with no build behind them
Posts that promote one model, wrapper or platform
Benchmarks copied from vendor announcements
Previously published posts republished from another site
Thin content under 800 words with no shipped work behind it

What you get

Your article in front of an engaged audience of business owners, marketers and founders
One relevant, contextual link back to your own site
A byline and the credibility of an established, high-traffic blog
Editorial help to make the piece as strong as it can be before it goes live

How to submit

Send your pitch or draft through the contact form. Include the topic, the one specific problem the post solves, and a line on your first-hand experience with it. One idea per pitch works best. You will hear back on whether it is a fit before you write the full draft.

What a strong large language models pitch looks like

A weak pitch says "I want to write about what large language models are and how they work." A strong one says "We shipped a support assistant on an LLM, added retrieval to stop it inventing policy, and cut wrong answers from one in six to one in forty, so I want to show the setup and the eval we ran to prove it." One is a brochure. The other is worth reading.

The angles that get accepted fastest

A shipped LLM feature and how you made it reliable
Hallucination handling that held up in production
A cost problem and how you controlled it
A build a similar team could scope this week
A contrarian view on where LLMs are the wrong tool

Looking for more places to pitch? The free write for us finder lists sites that take guest posts in your niche, and the free backlink checker shows what a placement is worth.

Frequently asked questions

Does Lilach Bullock accept large language models guest posts?

Yes. LLMs guest posts are welcome from a builder, product lead or founders and similar writers who have done the work and can share what happened. We publish practical articles with real examples and decline explainers of how large language models work.

What large language models topics do you accept?

We accept shipping features on large language models, handling hallucinations in production, prompt and context design, controlling cost, model choice trade-offs, retrieval and tools, and evaluating output quality. The common thread is a specific lesson with a result attached, not a general overview of large language models.

Can I include a link back to my own site?

One contextual link back to your own site is fine when it is relevant and helps the reader. Posts written purely to build a backlink with no real value are not published.

Do you accept previously published content?

No. Every submission must be original and not published anywhere else first, including your own blog, Medium, LinkedIn, or a guest slot on another site under a different name. Republished or lightly reworded content gets declined, and we check before it goes live.

How do I submit a large language models guest post?

Send your pitch through the contact form with the topic, the specific result or lesson you will cover, and a line on your own experience doing the work you are writing about. One idea per pitch works best, and you will hear back before writing a full draft.

Do you accept sponsored or commercial partnerships?

Commercial and partnership enquiries are handled separately from editorial guest posts. If you are a brand or agency wanting to work together rather than pitch an article, say so in the contact form and you will get the right details back.

How long should a large language models guest post be?

There is no fixed word count, but posts that do well here usually run from 1,000 to 1,800 words. Long enough to show the example and the numbers, short enough that a busy owner finishes it. Cut anything that repeats what the reader already knows.

What makes a large language models pitch stand out?

A real result in the first sentence. Say what you did, what changed and over what period, then say what the reader will be able to copy. For example: We shipped a support assistant on an LLM, added retrieval to stop it inventing policy, and cut wrong answers from one in six to one in forty, so I want to show the setup and the eval we ran to prove it.

Brands and agencies: sponsored posts and link placements on this blog are handled separately from editorial guest posts. See how to work with me or email [email protected].