Hi everyone 👋 I've been on a months-long journey to find the perfect SaaS tool to automate the creation of detailed show notes for my team's weekly podcast. My latest experiment was with SuperAGI, and I have to share my results because, while promising, the issue of factual hallucinations became a significant blocker for a production workflow.
I set up a fairly comprehensive agent with the goal of ingesting a one-hour audio file (transcribed via a separate, reliable service), and outputting structured show notes including: key takeaways, guest bio highlights, timestamped chapter markers, and relevant links mentioned. The setup process itself was quite engaging. I appreciated the granular control in the agent configuration, defining the specific tasks and the desired output format in the instructions. I used the Google Search tool for fact-checking alongside the core logic, hoping it would anchor the responses.
However, the consistency just wasn't there. Here are a few specific examples of the types of hallucinations I encountered:
* **Guest Background Inventions:** For a guest who is a startup CFO, the agent generated a bullet point stating they had previously authored a popular book on blockchain accounting. A quick Google search confirmed no such book exists. The agent seemed to infer "finance" + "tech" and fabricated a credible-sounding credential.
* **Fictitious Citations:** The agent would often include "Learn more at [website]" for tools discussed. In several runs, it generated URLs for products that were similar to, but not exactly, the ones named in the conversation. The URLs sometimes led to 404 pages or entirely different companies.
* **Misattributed Quotes:** It would occasionally create powerful, concise quotes that perfectly summarized a segment... but they were never actually said by the speaker. It was paraphrasing and extrapolation presented as direct quotation.
From a migration perspective, I was documenting this as a potential replacement for our current manual process. Here's my breakdown of where it currently stands:
**The Potential (Why I kept testing):**
* The framework for automation is clearly there. The ability to chain tasks is powerful.
* When it stuck to generic summarization without specific claims, the output structure was excellent.
* The open-source nature means this is a problem that could be solved with better tooling or fine-tuning.
**The Blockers (Why I can't switch yet):**
* **Data Quality Risk:** For public-facing content, factual inaccuracies are a non-starter. It adds a verification burden that negates much of the time savings.
* **Unpredictability:** You can't reliably predict *when* it will hallucinate. One episode would be 95% accurate, the next would have several glaring fabrications.
* **Configuration Complexity:** Mitigating this seems to require a deep dive into custom tool creation, more precise prompting, and possibly fine-tuning models—moving from a SaaS solution to a complex development project.
I'm left wondering if others have hit this wall with SuperAGI for content generation tasks. Have you found a configuration pattern, a specific tool combination, or a validation workflow that reliably reduces these factual errors? I'm comparing this to other AI writing agents, and the hallucination rate feels notably higher, though the customizability is also greater.
My search for a fully automated show notes SaaS continues, but I'm documenting everything. Hopefully this detailed experience is useful for anyone considering a similar use case