How content is created
PromptingEasy may use AI tools to help explore a topic, organize notes, compare wording, or prepare an initial draft. AI-generated text is not treated as a source. The published page remains the responsibility of the human editor.
The page is given a defined audience, purpose, level, and set of claims that need support.
Explanations and examples are written or revised for clarity. AI assistance may be used, but unsupported statements are not accepted merely because a model produced them.
Material factual and technical claims are checked, limitations are added, and language that implies guarantees is removed unless evidence supports it.
The page is published with an update date. Important later corrections are recorded in the public revision history below.
Different levels have different editorial goals
Beginner pages prioritize clear explanations and practical examples without pretending that complex systems are simpler than they are. Advanced and expert pages are reviewed for technical accuracy, precise terminology, implementation-dependent caveats, and production trade-offs. They may assume prior knowledge and are not simplified merely to make every section accessible to a beginner.
How facts are checked
The preferred evidence depends on the claim. When available, PromptingEasy prioritizes primary and authoritative sources over summaries or marketing material.
- First choice: official product documentation, standards documentation, original research papers, government publications, and first-party technical announcements.
- Supporting sources: reputable secondary explanations may provide context, but they do not replace a primary source for an important technical or time-sensitive claim.
- Practical examples: examples are presented as illustrations or heuristics, not universal performance guarantees.
Time-sensitive information
Model capabilities, product interfaces, prices, hosting limits, SEO features, laws, and provider recommendations can change quickly. Claims in these areas are checked close to publication or revision and should include a date or limitation when the timing matters.
Advanced and expert technical review
Technical pages are checked for more than surface-level plausibility. The review considers definitions, assumptions, algorithmic guarantees, runtime and hardware dependencies, failure modes, and whether a statement applies generally or only to a particular implementation. Relevant topics include retrieval, sampling, transformers, agents, tool calling, quantization, speculative decoding, fine-tuning, evaluation, latency, reliability, and cost.
Correction and update policy
Content is reviewed again when official guidance changes, a model or product materially changes, a reader reports a credible issue, or an internal review finds that wording is inaccurate, outdated, unsupported, or too absolute.
- Assess the claim. The reported passage is compared with the most relevant available sources.
- Correct the page. Material errors are fixed directly. Ambiguous claims may be narrowed, dated, sourced, or rewritten as a heuristic.
- Check related pages. Repeated claims and build-source content are reviewed so that the same issue is not reintroduced later.
- Record major revisions. Substantive changes are summarized in the public history. Minor spelling, grammar, formatting, and accessibility fixes may not be listed.
No review process can promise perfect or permanently current content. Readers should verify high-stakes medical, legal, financial, safety, and compliance decisions with qualified professionals and current authoritative sources.
Public revision history
This log records major editorial revisions. It is intentionally shorter than the internal line-by-line change record.
Prompting tutorials: unsupported guarantees, fixed success percentages, and overly absolute claims about prompt quality, personas, examples, and chain-of-thought instructions were qualified or removed.
LLM Advanced: RAG failure analysis, the non-standard “Large Action Model” label, MCP, A2A, and multi-agent trade-offs were technically clarified.
LLM Expert: speculative decoding guarantees, quantization performance dependencies, and the appropriate role of LoRA versus retrieval were made more precise.
Generators and use cases: model recommendations and workflow estimates were reframed as time-sensitive or task-dependent guidance rather than permanent rankings or guaranteed outcomes.