gemini-blog

Blog post generation with Google Gemini uses a two-step flow: first analyzing the topic, then producing structured output via postSchema and highlightSchema. Configure prompts in packages/ai/config.ts, set model parameters, and enable Langfuse telemetry to track quality and reduce hallucinations across your pipeline.

Inconsistency and hallucinations typically stem from weak prompt configuration or missing schema validation. This Skill applies quality checks through structured output schemas and enforces a deterministic two-step generation flow, ensuring consistent tone and factual accuracy across blog posts.

Yes. Modify postSchema and highlightSchema in your configuration to support new formats, then update prompts in packages/ai/config.ts. The two-step generation flow and type-safe outputs adapt to new formats while maintaining telemetry instrumentation and idempotent persistence.

Langfuse telemetry integration provides observability into prompt execution, model responses, and structured output validation. Check logs for schema mismatches, model parameter effects, and prompt effectiveness to identify bottlenecks in your Gemini-powered generation workflow.

You need Google Gemini API access, Vercel AI SDK, a blog post schema (postSchema and highlightSchema), and Langfuse configured for telemetry. The Skill enforces type-safe outputs and idempotent persistence, requiring no additional external dependencies beyond these core tools.

Yes. The two-step generation flow, configurable schemas, and telemetry instrumentation scale across multiple post types and formats. Idempotent persistence ensures reliability, while prompt tuning and model parameter adjustments optimize consistency and reduce costs at volume.