I keep seeing the hype about Gemini 1.5 Pro's long context window, but I'm skeptical. The demos are cool, but I need real-world feedback before I'd consider switching from GPT-4 or Claude for any production workflows.
Anyone here actually using it for something serious? Like:
- Processing large documents for data extraction or summarization.
- Automating content generation where quality/reliability is critical.
- Any integrations with marketing automation or CRM platforms?
I'm most curious about the actual output quality for practical tasks, not benchmarks. Does it follow complex instructions as well as it claims? And what about speed and cost once you're past the free tier? 😅 The token pricing structure seems complex.
We're running it in a very limited pipeline for parsing messy, multi-format customer feedback exports. The long context is real, I'll give it that. You can shove a truly stupid amount of text in there.
But on your core question, following complex instructions? It's inconsistent in a way GPT-4 just...isn't. For simple extraction it's fine. Give it a nuanced formatting rule or a conditional logic step and it'll sometimes follow it perfectly, other times it'll just invent a new task. It feels clever until it subtly misses the point, which is worse than being obviously wrong.
The speed is fine for batch jobs, but the pricing gets weird fast if you're actually using that context window. It's not a drop-in replacement, more like a specialized tool for when you absolutely need to process a novel-length document in one go and can afford to validate its work.
Data over dogma.
That's exactly where I am right now. I'm trying to evaluate it for a very specific onboarding use case, parsing and summarizing internal project post-mortems and legacy training docs to build a new employee knowledge base. The long context seemed perfect, as some of these documents are massive PDFs with hundreds of pages.
But I've hit the same inconsistency wall with instruction following you're asking about. If I ask for a summary in a specific three-part format, it might get the structure right but then omit a crucial section from the source doc, or it will invent a section that wasn't there. It feels like it understands the words of my request but not the intent, which is frustrating for anything that needs to be reliable. I'm not even at the point of worrying about cost yet because I can't trust the output quality for my critical task.
Have you found any particular type of instruction or prompt structure that makes it more reliable for you, or is it just a fundamental trade-off with the model right now?