Hey everyone! I've been deep in the content workflow trenches lately, trying to wrangle consistency across our sales enablement and demand-gen materials. You know how it is—one great output is a happy accident, but repeating it is the real challenge.
So, I finally organized my experiments into something shareable: a public repository of tested content generation "recipes." These are the step-by-step workflows that actually worked for me, especially when blending CRM data with AI tools.
Think of it as a cookbook for predictable, high-quality content output. Right now, it includes:
* **Brief Templates:** For blog outlines and social posts, structured to pull insights from our CRM's conversation intelligence data.
* **Review Checklists:** A human-edit stage guide to ensure the AI's output aligns with our sales voice and compliance rules.
* **Multi-Tool Pipelines:** My favorite setup for turning a single sales call transcript into a nurture email, a battle card update, and a pipeline risk alert.
It's all about creating a repeatable process, not just a one-off cool result. I'd love for you to check it out, use what works, and maybe even contribute your own recipes. Let's build this resource together!
— Aiden
Let the machines do the grunt work
Repeatable process is the right angle. The big fail point I've seen is recipes that ignore data structure. Your mention of blending CRM data is the key detail.
What does your pipeline actually read from the conversation intelligence data? Are you querying a specific view, or is it a raw JSON export? That's where most of these workflows fall apart - the recipe assumes clean data but the source tables are a mess.
If your repo has notes on the expected data shape or even a sample schema, that's more valuable than the prompt templates themselves.
Show me the latency.
You're dead on about data structure. The recipes assume you've already done the extraction and basic cleaning. In my case, it's pulling from a pre-built analytics view, not raw tables. The repo includes a README section on that exact point - the expected input is a flattened CSV of key phrases, pain points, and common objections aggregated per client segment.
If someone's starting with a raw JSON export, they'll need a separate transformation step before any of these recipes are useful. That's the real work, honestly. The templates just give you something to pipe the clean data into.
been there, migrated that