AI Content Generation implementation checklist

Define a system prompt that includes explicit 'do not use' word lists, tone descriptors (e.g., 'technical but accessible'), and persona constraints to prevent generic AI phrasing.

Include at least 3-5 high-quality, human-written content examples in the prompt context to guide the model on structural and stylistic expectations.

Set temperature between 0.7 and 0.8 for creative writing or 0.2 for factual reporting to ensure consistent output quality across multiple runs.

Specify exact header hierarchies (H1, H2, H3) and list formats in the prompt to ensure the output integrates directly with the frontend components.

Verify the model adheres to negative constraints, such as 'do not mention competitors' or 'do not use passive voice', through 20+ test iterations.

Validate the JSON output from the LLM against a strict Zod schema to ensure required fields like 'title', 'meta_description', and 'body' are present and correctly typed.

If using Retrieval-Augmented Generation, verify that every factual claim in the output is mapped to a source chunk in the vector database.

Implement a regex or LLM-based scanner to verify that all generated URLs, statistics, and citations are valid and reachable.

Scan generated content for common AI placeholders like '[Insert Name Here]' or '[Link to Source]' before it reaches the publishing queue.

Integrate an API like Copyscape or Turnitin to check generated drafts against existing web content to avoid duplicate content penalties.

Implement exponential backoff and retry logic in the generation service to handle 429 errors from LLM providers during peak loads.

Offload content generation to an asynchronous worker (e.g., BullMQ, Celery) to prevent timeout issues during long-form text generation.

Configure a secondary model (e.g., falling back from GPT-4 to Claude 3) to maintain service availability if the primary provider experiences downtime.

Implement per-user or per-request token tracking to monitor costs and alert when generation exceeds predefined budget thresholds.

Store the prompt ID, model version, and temperature alongside each generated piece of content to allow for A/B testing and regression tracking.

Generate unique meta titles and descriptions constrained to 60 and 160 characters respectively, including the primary keyword.

Integrate a script that scans the generated text for keywords and automatically suggests or inserts links to existing high-authority pages on the site.

Calculate the Flesch-Kincaid grade level of the output to ensure it matches the target audience's reading level (e.g., grade 8-10 for general blogs).

If images are generated or attached, use a vision model or prompt to create descriptive, SEO-friendly alt text for every asset.

Verify that the generated content naturally incorporates target keywords without exceeding a 2% density threshold to avoid keyword stuffing flags.

Implement a 'Pending Review' state in the CMS that requires a human editor to sign off on AI-generated content before it goes live.

Ensure the UI includes a disclaimer or metadata tag identifying the content as AI-assisted where legally or ethically required by the platform.

Run a pre-processing script to strip any Personally Identifiable Information (PII) from user inputs before sending them to third-party LLM APIs.

Build a UI mechanism for editors to 'thumbs up/down' or edit content, feeding those corrections back into the prompt context for future runs.

Set a flag for AI-generated content to be re-validated every 6 months to ensure facts and links remain current.