I’ve been curious about the AI Copilot Workflow Generation feature. The pitch is that you describe what you want in plain text and it builds a workflow for you. That sounds useful, but I’m skeptical about whether it actually works for something as complex as a retrieval-augmented content creation workflow.
Like, if I say “build a workflow that pulls trend data and internal briefs, then uses that to draft social media posts,” does the Copilot actually understand that I need retrieval steps, data formatting, and generation steps? Or does it just create a generic template I have to hack apart and rebuild anyway?
And more importantly, after it generates the workflow, how much customization do I actually need to do? Because if I’m spending an hour writing descriptions and then another two hours fixing what it built, that’s not faster than just building it visually from scratch.
Has anyone actually used the AI Copilot for a RAG workflow and had it work well, or does it mostly create half-baked scaffolding?
The Copilot is legit for RAG workflows. I’ve used it multiple times, and it’s not magical, but it’s genuinely useful.
The key is being specific in your description. Don’t say “pull trend data.” Say “search Twitter API for posts mentioning [topic], filter by engagement, extract hashtags and sentiment.” The more concrete you are, the better the generated workflow.
What the Copilot does really well: it understands retrieval patterns. You describe needing to fetch data, and it builds those fetch steps. You describe needing to combine data for a model, and it adds transformation steps. You describe needing to generate content, and it sets up the generation step.
After generation, you usually need 20% customization. Not 80%. Maybe adjust API parameters, add a filtering step, tune the generation prompt. But the skeleton is solid.
I’ve deployed Copilot-generated workflows to production with minimal iteration. Try it with your trend + brief workflow. You’ll spend an hour describing it well, maybe 30 minutes tweaking the output, and you’re done.
I used the Copilot for a newsletter workflow that needed to retrieve article summaries and generate commentary. The generated workflow had the right structure: fetch articles, process them, generate text. I customized the fetch queries and the generation prompt, deployed it, and it worked.
The 20-80 rule applies in reverse here. 20% of effort is writing the description, 80% of effort is tweaking the generated workflow to match your data and style. But compared to building from scratch, it saves real time.
One tip: describe your retrieval requirements clearly. Instead of “get relevant data,” say “search by keyword, limit to last 30 days, sort by relevance score.” The Copilot responds better to specific retrieval logic. For generation, describe the output format you need. The more explicit you are about inputs and outputs, the better the workflow structure.
The Copilot works if you describe workflows clearly. Vague descriptions produce vague outputs. Specific descriptions create valid workflow skeletons. For RAG workflows specifically, it understands retrieval patterns well because that’s a structured concept—fetch, transform, search, generate.
I’d estimate the Copilot saves 40-50% of workflow-building time. You describe clearly, review the output, customize specific steps, deploy. Faster than building from scratch, slower than using a marketplace template, but flexible enough to handle specific requirements.
Copilot works well if you describe specifics. vague → vague output. clear specifics → valid skeleton. saves 40-50% build time. 30 min tweaking usually needed.