Editorial Policy
Last updated: August 29, 2026
AI Tech Ledger publishes AI and technology news, reviews, comparisons, tutorials, installation guides, and practical explainers. Our editorial process is designed to prioritize accuracy, original reporting or analysis, transparent sourcing, reader usefulness, and clear corrections.
How We Select Topics
We focus primarily on artificial intelligence, AI models and agents, open-source AI, APIs, AI tools, local AI, practical technology guides, software releases, and major technology developments that have a clear impact on users. We do not publish a story simply because a topic is trending; it should offer useful information, original context, testing, analysis, or a practical takeaway.
Primary Sources First
Whenever possible, we verify important claims against primary sources such as official documentation, developer repositories, release notes, research papers, product pages, company announcements, regulatory material, and direct public statements. Secondary sources may be used for context, but they should not replace an available primary source for a factual claim.
Original Testing and Analysis
Reviews, comparisons, and guides should add value beyond repeating an announcement. When practical, we test products, models, APIs, workflows, installation steps, prompts, or features ourselves and explain what we observed. Screenshots, benchmark interpretation, pricing checks, limitations, response examples, and reproducible steps may be included when they help readers evaluate a claim. Our detailed process is explained in the Review Methodology.
Headlines and Images
Headlines should accurately describe the article and should not promise something the article does not deliver. We avoid misleading clickbait, exaggerated claims, and artificial urgency. Featured images should be relevant to the article, high quality, and suitable for large previews. Our preferred featured-image format is a landscape image of at least 1200 pixels in width, with 1280 × 720 pixels used as the default target where practical. Images should not rely on excessive embedded text to communicate the story.
AI-Assisted Work
AI tools may be used to support research organization, drafting, editing, coding, summarization, or workflow efficiency. They are not treated as authoritative sources. Important factual claims must be checked against reliable sources, and final editorial responsibility remains with AI Tech Ledger.
Reviews, Comparisons and Commercial Relationships
Reviews and comparisons should identify meaningful strengths, limitations, pricing considerations, compatibility issues, and relevant alternatives. Sponsored material, affiliate relationships, free review access, or other commercial relationships that could affect how readers interpret an article should be disclosed clearly where applicable. Commercial relationships do not change our obligation to distinguish fact from opinion.
Corrections and Updates
Technology changes quickly. We may update articles when software, pricing, documentation, availability, benchmarks, security information, or other material facts change. Minor clarity edits may be made without a separate notice. Meaningful factual errors should be corrected promptly. Readers can report a correction by emailing contact@aitechledger.com with the article URL, the specific issue, and a reliable supporting source when available.
Authorship and Accountability
Articles may be published under an individual contributor or the AI Tech Ledger Editorial Team. The byline identifies the party responsible for reviewing and publishing the article. Editorial-team content follows the same sourcing, accuracy, originality, and correction standards described on this page.
Our Publishing Standard
Before publication, important articles should be checked for a clear headline, useful opening, accurate dates, reliable sources, appropriate internal links, a relevant large featured image, descriptive alternative text, mobile readability, and a clear distinction between confirmed facts and commentary. Our goal is to publish content that remains useful even when a reader arrives without knowing the original news event or search query.