Practical guidance for writing clear, accurate, and useful articles for the Ploba community. Applies to both guest and sponsored contributions.
Core principle
Write for the reader who is one step behind you. They are capable and motivated but have not yet solved the problem you are writing about. Explain your reasoning, not just your conclusion. Show what a step achieves, not only how to do it. Be honest about limitations and trade-offs.
Section 1
The first two or three paragraphs determine whether a reader continues. A strong introduction names the problem clearly, says who the article is for, and gives the reader a reason to keep reading.
A good introduction does three things in order:
Avoid
Large language models are everywhere these days. Many developers are integrating them into their applications. This article will look at how to do that.
Prefer
When you call an LLM from application code, you need to handle three things that most getting-started guides skip: token budget, retries on transient errors, and cost attribution per feature. This article shows you how to address all three using the OpenAI Python SDK. It assumes you have already made a successful API call and you want to move from a proof of concept to something you can run reliably in production.
Section 2
Link text should tell the reader where the link goes and why it is relevant — before they click. Screen readers announce link text in isolation, so "click here" or "this article" is inaccessible and uninformative.
Avoid
For more information on rate limiting, see this page.
Prefer
The OpenAI documentation on rate limits and usage tiers explains how token-per-minute quotas are applied per model.
Section 3
Every factual claim that a reader might reasonably question should be supported with a link or an explanation of how you know it. This includes benchmarks, performance comparisons, and market-size figures.
Official documentation
Use for describing how an API or model behaves. Strongest source for technical claims.
Original research or papers
Use for benchmark results and capability claims. Link to the paper, not a summary.
Your own testing
Valid if you describe your conditions clearly: model version, prompt, dataset, date.
Social media posts
May support what the poster said or announced. Not evidence of a product's performance.
Wikipedia
Acceptable for background context. Not a sole source for technical or disputed claims.
Avoid
GPT-4o is significantly faster than GPT-4 Turbo for most tasks.
Prefer
In OpenAI's May 2024 benchmark release, GPT-4o achieved median response times around 320 ms on text-only prompts — roughly half the latency reported for GPT-4 Turbo in comparable conditions. Latency varies with prompt length and server load, so measure in your own environment before committing to a latency SLA.
Section 4
A tutorial walks a reader through completing a specific task. Structure your tutorial with four parts: prerequisites, numbered steps, expected results, and limitations.
01
Prerequisites
List everything the reader needs before they start. Include software versions where they matter. Link to setup guides rather than inlining the instructions.
02
Numbered steps
Each step should do one thing. State what you are about to do, show the code or command, then explain what it does and why it matters.
03
Expected results
After each significant step, show what success looks like: terminal output, API response structure, or UI state. If the result varies, say so.
04
Limitations
End with an honest summary of what the approach does not handle. Context window limits, cost at scale, model version drift, latency requirements.
Section 5
A comparison is only useful if it uses consistent criteria across every item. Run the same prompt, on the same date, with the same settings, and report the same metrics for each model or tool.
Required disclosures for any comparison:
Example disclosure
All tests run on 14 September 2026 using default temperature settings. Prompts available at github.com/example/llm-comparison-prompts. The author has no commercial relationship with any of the vendors tested.
Section 6
When you write a guide for a specific AI tool, clearly separate what you observed from firsthand testing from what the vendor claims. Vendor documentation and marketing materials are not independent evidence.
Vendor claim — label it
According to Anthropic's product page, Claude 3.5 Sonnet achieves state-of-the-art performance on the SWE-bench coding benchmark.
Prefer
In my testing on 12 September 2026, using a set of 20 internal debugging tasks from a TypeScript codebase, Claude 3.5 Sonnet correctly identified the root cause in 17 of 20 cases. GPT-4o identified 15 of 20. Both models occasionally suggested fixes that did not compile; I excluded those from the count.
Disclosure
If you are an employee, contractor, investor, or affiliate of the tool's maker, disclose this at the start of the article. If you received a free account, extended quota, or any other benefit from the vendor in connection with writing the article, disclose this too.
Section 7
You may use AI tools to help with drafting, editing, or generating code examples. You may not submit output from an AI model as your own original insight.
Disclose AI assistance in your submission cover note. Include:
Editors will assess whether the article demonstrates genuine knowledge of the subject. A well-edited AI-assisted article showing deep expertise and accurate advice is acceptable. An AI-generated article padded with generic advice and no firsthand perspective will be declined.