▲ 4 r/u_bin95blog+2 crossposts

Does the llm.txt file really work?

And other AI LLMS text file questions answered for deep insight.

TL;DR

Yes, llms.txt does work — but only when you build all four LLMS files (llms.txt, llms‑ctx.txt, bing‑llms.txt, google-ai-overviews.txt) and follow a deliberate process. Most advice online is incomplete or flat‑out wrong. I spent days refining each file with the top AI models and documented what actually matters: company‑first structure, authority signals, consistent sections, and letting each AI model grade and improve its own file. This post answers the most common LLMS questions and gives you a practical section list you can use to build a best‑in‑class llms.txt for your own site.

Why I wrote this

Most LLMS.txt advice online is either incomplete or flat‑out wrong. After building all four LLMS files for a large, complex site, I wanted to share what actually works so others don’t have to spend days figuring it out.

Hi, fellow Redditors. I spent many hours/days working with the top 4 AI models to create near‑perfect LLMS text files for them. So I thought I’d share insights and answers to the most common questions, so others won’t have to work as hard. First, a disclaimer for SEO professionals thinking, “Hours? LLMS.txt is easy — shouldn’t take that long.” You’re correct if you have a small website with a couple dozen pages and one or two products. But when you’re dealing with a large site with hundreds of pages, diverse offerings, a unique business model, and a 30‑year company history… let’s just say you learn a lot about how to make LLMS files actually work. 🤪 Let’s start with the main question.

Does the llm.txt file really work?

Yes — especially for companies with large or complex websites. For smaller sites, it’s more about preparing for future needs. But regardless of size, LLMS files only work if done properly, and the process I used helps ensure they actually impact AI visibility. And when I say “yes, it works,” I’m referring to implementing all four LLMS files, not just llms.txt. Context is everything. You may have seen headlines like “Analysis of 300,000 domains shows llms.txt doesn’t impact how AI systems see or cite your content.” Catchy title — but dig deeper. Only ~30K sites even had llms.txt. No breakdown of:

  • site size
  • industry
  • complexity
  • number of offerings
  • whether the file was implemented correctly
  • whether the other three LLMS files were used

All of those matter. That article could be summarized in one sentence: About 10% of sites implemented llms.txt, and the other three LLMS files weren’t mentioned at all. Below are answers to the most common questions to help you understand the reasoning behind my approach.

What is an llms.txt file?

Contrary to what many SEO professionals say, llms.txt is not “a curated guide to your website.” It’s a curated Markdown guide to your company, designed to help AI agents understand your trusted content.

Is llms.txt a real thing?

Yes — it’s a new standard in the making, similar to how robots.txt started. But llms.txt is only ¼ of the system. You should create all four files:

llms.txt

bing-llms.txt

llms-ctx.txt

google-ai-overviews.txt

And no — llms-full.txt is not worth the effort. AI token limits mean they often only digest the first ~40 lines of llms.txt anyway. Sitemaps and robots.txt already cover full URL lists. llms-full.txt is something to revisit next year.

Does adding an llms.txt file improve SEO or AI visibility?

Not directly for traditional Google rankings. But it does increase the likelihood that AI models will accurately cite your company and understand your offerings. For large sites or sites with weak SEO in certain areas, LLMS files can help AI discover content it previously missed. There’s also a crossover effect between AI models.

Do major AI systems or Google use llms.txt?

Yes — especially when you implement all four files and keep them consistent and best‑in‑class. Here are the scores our files received (1–10), rated by each AI model:

  • OpenAI LLMS: 9.8 (llms.txt)
  • Bing/Perplexity LLMS: 9.9 (bing-llms.txt)
  • Claude LLMS‑CTX: 9.5 (llms-ctx.txt)
  • Google AI Overviews: 10 (google-ai-overviews.txt)

Google’s file is tiny and easy, but surprisingly, many don’t reach a score of 10.

What is the biggest and most common failure when creating llms.txt files?

Designing it with a website‑first approach instead of a company‑first approach. I started with “What do I want AI to know about our website?” After many iterations — and after each AI model critiqued its own file — the structure shifted toward a company‑first approach. Sections like:

  • company metadata
  • structured data
  • primary audiences
  • competitive differentiators
  • FAQ
  • published work
  • industry recognition
  • intellectual property
  • owned brands
  • entity aliases

…all became essential.

Does llms.txt replace robots.txt or sitemap.xml?

No. LLMS files are for AI ingestion. Robots.txt and sitemaps are for search engines and scrapers. Different tools, different purposes.

How to make an llms.txt file

Create a new llms.txt file and describe your company and website using plain‑text Markdown. Upload it to your website root so AI crawlers can find it. Then add this rule to your robots.txt:

Allow AI crawlers to access LLMS files
User-agent: * 
Allow: /llms.txt 

See the section list below for guidance. If you want a link to a best‑in‑class example, ask in the comments. After your first draft, ask ChatGPT to score it from 1 to 10. Implement the suggestions, then ask again. Repeat until your score is 9 or higher. Note: Some websites (including ours) block ChatGPT’s user window from accessing the file directly. That does not mean the bot can’t access it — only public users. If that happens, just paste the file content into ChatGPT.

What should be in an llms.txt file?

An llms.txt file should give AI models a clean, factual snapshot of your company: who you are, what you offer, who it’s for, and why you’re authoritative. It’s not a marketing page — it’s a structured knowledge file. llms.txt is the master file. Build it first, polish it, then reuse its sections across llms‑ctx.txt, bing‑llms.txt, and google-ai-overviews.txt.

Example llms.txt section list (use what applies, skip what doesn’t)

1. Company name and aliases

  • One‑sentence identity summary

2. Website root

  • Main homepage URL

3. Company metadata

  • Founding year, ownership, certifications, BBB rating, etc.

4. Structured data

  • Delivery modes (online, offline, video, onsite)
  • Types of offers (courses, software, services)

5. Last Updated

  • Helps AI know the file is current

6. Primary audiences

  • Who your products are designed for

7. Core products and services

  • High‑level product categories

8. Search keywords

  • Terms users associate with your brand or offerings

9. Competitive differentiators

  • What makes your products different or better

10. Product relationships

  • How your products connect or complement each other

11. “If user wants…” sections

  • Helps AI map user intent to the right product

12. Categories (for large or diverse sites)

  • Organized product or content groupings

13. Frequently Asked Questions

  • Short factual answers to common queries

14. Authority pages

  • About, reviews, certifications, trust signals

15. Published work and industry recognition

  • Trade magazines, media features, long‑term credibility

16. Citations and academic references

  • Papers, journals, research that reference your work

17. Intellectual property

  • Trademarks, proprietary methodologies, patents

18. Product ecosystem (if applicable)

  • Brands, sub‑brands, product families

19. Entity aliases

  • Alternate names users or publications use

20. Preferred citation pages

  • Pages AI should reference when summarizing your brand

21. Contact information

  • Support, sales, general inquiries

Why these sections matter

  • Authority pages → help AI trust your brand
  • Product relationships → help AI recommend the right product
  • Competitive differentiators → help AI explain what makes your offerings unique
  • Entity aliases → prevent misclassification
  • Preferred citation pages → reduce hallucinations
  • Search keywords → help AI map user queries to your content

How to test llms.txt

Ask the AI platform for the file it is designed for. It’s no different than asking a customer for feedback — you’re explaining your company to each AI model in the way it understands best.

Are major AI crawlers actually reading llms.txt yet?

Yes — if you follow this guide. No — for everyone else. SEO professionals often answer from a technical standpoint rather than from intent. It’s true that AI models aren’t “seeking out” llms.txt the way robots.txt or sitemaps are. But that negative reply is based solely on llms.txt — not the other three files. Even without directly opening llms.txt, the process above gets your information into the AI’s knowledge base. And when all four LLMS files are consistent, AI models treat your content with higher credibility. The bottom line: you want AI to understand your business more fully and clearly. LLMS files help you do that.

Advanced FAQs (often asked by developers or security‑minded users)

These questions come from people with a web development or data privacy background. They’re not essential for building an effective llms.txt file, but they help clear up misunderstandings.

Can llms.txt be used to block AI scrapers or prevent training?

No. LLMS files are informational only. Use server‑level controls (like htaccess rules) to block specific bots.

Should llms.txt include only key pages or a full sitemap?

Only key pages. Think of it as a “best foot forward” file. Include the pages that define your brand, establish your authority, and outline your core offerings.

If you want me to share links to our best‑in‑class LLMS files or have a question, just ask in the comments.

reddit.com
u/bin95blog — 3 days ago
▲ 0 r/u_bin95blog+1 crossposts

How to learn PLCs?

If you’re serious about learning PLCs, this is the full 10‑phase path. Fair warning: it’s not a “take one course and you’re done” situation. Real‑world PLC work demands more depth than most online classes provide. But if you stick with all 10 phases, you’ll actually be prepared for what the field throws at you.

Based on the recommended training path from the PLC Programming Training Organization, the PLC training process is broken down into the following ten sequential learning phases:

1. PLC Prerequisites

Before diving into PLCs, you must build a foundational understanding of the underlying systems they control. This phase covers:

  • Electrical basics
  • Industrial control

2. PLC Training Foundation

This phase introduces the core concepts necessary for safely managing and working around PLCs in an industrial environment. It covers:

  • PLC basics
  • Safety and reliability
  • Best practices and commissioning
  • Managing PLCs in a facility

3. PLC Training Scholastics

Focuses on the academic and technical principles behind how these systems operate. It covers:

  • PLC fundamentals
  • PLC hardware and operation
  • PLC programming basics
  • PLC implementation

4. PLC Troubleshooting

A critical phase for hands-on workers to diagnose and fix automated systems when things go wrong. It covers:

  • Problem-solving basics
  • PLC troubleshooting best practices
  • Gaining experience in troubleshooting faults using a PLC

5. Hands-on PLC Training

Practical application using physical or simulated machinery. It covers:

  • Working hands-on with two or more major PLC brands (Allen Bradley and Siemens are recommended)
  • Uploading and downloading to a PLC
  • Modifying existing programs

Advanced PLC Training Phases

6. PLC Programming

Transitioning from basic modifications to building and designing automation logic. It covers:

  • Instruction examples
  • PLC programming best practices
  • Studying real-world programs
  • Completing programming projects

7. PLC Communications

Understanding how PLCs talk to other devices, networks, and upper-level systems. It covers:

  • Industrial networks and cybersecurity
  • RS232 and Fieldbus
  • Ethernet/IP, DeviceNet, ControlNet, and Modbus

8. PAC (Programmable Automation Controllers)

Moving beyond standard PLCs into more powerful, modern automation controllers. It covers:

  • Differences between a PLC and a PAC
  • PAC hardware and commissioning
  • Task-program priority and execution flow
  • Tags, data types, and motion control
  • The 5 core programming languages

9. More Advanced Topics

Advanced control loops and integrating industrial data with standard IT languages. It covers:

  • Proportional-Integral-Derivative (PID) loops
  • Introduction to the Python programming language
  • Introduction to SQL database queries

10. SCADA - HMI

Learning how to design and manage the visual interfaces operators use to monitor the factory floor. It covers:

  • HMI (Human-Machine Interface) overview
  • SCADA (Supervisory Control and Data Acquisition) overview
  • Hands-on experience with two or more SCADA brands
  • OPC (Open Platform Communications)

That’s the full learning arc. It takes time, but every phase builds real, usable skill. If you’re somewhere in the middle of this path and not sure what to focus on next, feel free to ask — lots of people here have been through it.

reddit.com
u/bin95blog — 1 month ago