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?
I share your skepticism and am evaluating it for a specific use case. We're testing it on ingesting long, unstructured employee policy documents to map them to standardized compliance frameworks. The context window is a legitimate advantage here.
However, my initial tests on instruction following for structured data extraction have been mixed. You asked about quality for practical tasks. It can pull a list of policy clauses from a 200-page document, but when instructed to tag each by a specific risk category and the relevant jurisdiction, it often invents categories or misapplies the rules from the prompt. The error is subtle enough that you have to check everything.
The speed for these large batches is acceptable, but the cost implications of using the full context window for many documents are still unclear to me. Have you found any clear analysis on the break-even point versus chunking with another model?
Your point about subtle misapplication of tagging rules resonates. I've seen that in my own structured extraction benchmarks. The model seems to grasp the categories themselves, but its assignment logic drifts, especially on ambiguous clauses. I'd argue it's a grounding issue.
On cost, I've done some preliminary math. If your average policy document is, say, 100k tokens and you process it whole, that's about $0.75 per doc with Gemini 1.5 Pro. Chunking that same doc into ten 10k-token chunks for GPT-4 Turbo would run about $1.00. So the break-even is real, but it's fragile; if the model's inconsistency forces a significant manual review or correction step, any cost advantage evaporates immediately. The long context only pays off if it also delivers proportionate accuracy, which seems to be the real hurdle.
-- bb42
You're right to be skeptical. I've been testing it for automating documentation from code repos - the exact use case where the long context should shine. For pure summarization of a monolithic codebase dump, it's passable. The problem surfaces when you ask for anything structured, like generating API endpoint tables with specific field mappings.
It'll follow the format but silently drop fields or mislabel parameters, errors you can't catch without a full verification. That manual review overhead kills any potential cost savings from processing everything in one go. The inconsistency isn't a bug, it's a core characteristic for now.
sub-100ms or bust
That's a really sharp observation about the difference between pure summarization and structured extraction. Your API endpoint table example is spot on.
I've been trying to use it to generate data model diagrams from database schema dumps, and I've hit the exact same wall. It'll follow the Mermaid.js syntax I ask for, but then it will quietly swap a one-to-many relationship for a many-to-many one, or just omit a table entirely. It looks right at a glance.
That "silently drop fields" behavior feels like the biggest risk for production. How are you handling the verification step? Are you using a smaller, more reliable model to do spot checks on its output, or is it back to full manual review?
The hype is real on context window size, I've thrown entire API spec PDFs at it. But I've got to echo what others are saying about practical output.
>I'm most curious about the actual output quality for practical tasks, not benchmarks.
If your task is pure summarization of a huge doc, it's great. The moment you need structured, repeatable output from that context - like pulling specific fields into a CSV or generating a formatted table with strict rules - it gets unreliable. It follows the letter of your formatting instruction but misses the intent, dropping data points without warning. That's a non-starter for anything automated.
On cost, the break-even math is shaky. If its inconsistency adds even 10% manual review time, you've lost the price advantage over chunking with GPT-4. It's a specialized tool, not a general replacement.
Yeah, I'm testing it on something similar, pulling structured data from our old project briefs in Confluence. The long context is a game changer for that, honestly.
But you're right to ask about instruction following. I've found it needs its instructions broken down into much smaller, simpler steps than I'd use with GPT-4 for a similar task. Even then, I have to do a spot check on every fifth or sixth output. It just quietly skips things.
Does anyone have a reliable prompt pattern for these structured extractions, or is it always going to need a verification layer?
Exactly what I'm trying to figure out for our onboarding flows! I was so tempted to use it for summarizing massive customer feedback exports from our CRM and support tickets into neat journey maps.
The context window is perfect for that, but the reliability just isn't there yet for anything production-grade. I ran a test where I asked it to tag feedback by sentiment AND map it to a specific stage in our customer journey framework. It nailed the sentiment, but then kept assigning "post-sale" feedback to the "consideration" stage, which completely breaks the map. Like others said, it follows the instruction format but not the logic.
For now, I'm sticking with chunking + GPT-4 for anything that feeds into our actual systems. It's slower, but I don't have to babysit it.
You're hitting the nail on the head with "actual output quality for practical tasks." I've been testing it for processing user feedback archives from our support system, dumping thousands of tickets at once. For pure summarization, it's impressive.
But the moment I need structured output, like tagging each ticket with specific categories from our internal taxonomy and extracting a date, the model starts to fumble. It'll follow the JSON format I specify perfectly, but then quietly drop the 'priority' field or hallucinate a category that doesn't exist in my list. That inconsistency makes it impossible to trust for anything automated right now.
On cost, the math others have done is right - the advantage disappears if you need a verification layer. For me, that means it's back to chunking with GPT-4 until the reliability improves.
Your skepticism is totally justified. I gave it a real shot for production - automated infrastructure runbook generation from long monitoring alert histories.
The 1M context is a genuine feat, but your hunch about output quality vs. benchmarks is dead on. It'll digest a 300-page incident log beautifully. Ask it to produce a concise, formatted postmortem with specific root cause, impact duration, and remediation steps? It nails the format, then silently swaps the root cause for a plausible-but-wrong service, or gets the timeline wrong by an hour. The errors are subtle enough to slip past a tired ops engineer at 3 AM.
That inconsistency means you build a verification step, and at that point, the complex token pricing and any speed advantage just evaporate. Stick with GPT-4 for now, trust me. The demos are cool, but production needs reliability, not just raw context.
Yeah, that's exactly what I was wondering too. I've only tested it for summarizing meeting transcripts from our internal tools, which it handles okay. But your question about following complex instructions for production workflows - I'm not convinced yet.
Everyone here seems to have the same issue. It's great at digesting a giant doc, but the moment you ask for something specific like pulling data into a format for your CRM, it gets unreliable. It'll follow your template but skip fields randomly. That verification step seems necessary, which probably kills the cost benefit.
For now, I'm sticking with chunking smaller docs with the older models. Has anyone actually found a reliable way to use it with a marketing automation platform, or is it all still just testing?
No, not for production. I tested it for structured log ingestion from multi-source tracing data. The technical context ingestion works as advertised. The output is where it fails.
It will correctly parse span IDs and timestamps across a massive log dump, then silently misattribute parent-child relationships when generating the service dependency graph. The error isn't in format, it's in logical consistency. You get a perfectly formatted DOT file with a critical, silent error in the edges.
For cost, the verification step required to catch those errors negates any pricing advantage over chunking with GPT-4. The long context is an engineering marvel, but it's not a production-ready tool for reliable extraction yet.
Your use case with post-mortems and legacy docs is one I've seen fail in exactly the same way. It excels at digesting the content but stumbles on the extraction logic.
I ran a similar test with old incident reports, asking for a structured summary with sections for timeline, root cause, and remediation. It would consistently generate a beautiful template but then populate the root cause field with a contributing factor from three paragraphs prior, not the actual primary cause. The model seems to prioritize linguistic coherence and template completion over factual accuracy from the source.
I haven't found a prompt pattern that solves this fundamentally. Breaking instructions into atomic steps helps slightly, but the risk of omission or logical misattribution remains. For building a reliable knowledge base, you're likely better off using its long context for an initial digest, then using a smaller, more reliable model for the structured parsing step from that digest.