Hey everyone! 👋 I've been a big fan of Humata for quick Q&A on technical docs and research papers, but recently I've been testing Claude's file upload feature for some heavier infrastructure PDFs. I think I might be switching for certain tasks, especially with longer, complex documents.
My main use case is parsing AWS whitepapers, architecture guides, and lengthy Terraform module documentation (sometimes 100+ pages). With Humata, I'd sometimes hit limits on file size or get truncated answers when the context was spread across many pages. Claude's 200k context window seems to handle these monolithic PDFs much better.
Here's a concrete example from last week:
* I uploaded the **AWS Well-Architected Framework PDF** (the full thing).
* I could ask: "Based on the reliability pillar, draft a Terraform snippet for an auto-scaling group with health checks that follows their recommendations."
* Claude's response pulled concepts from different chapters and gave me a ready-to-tweak code block.
```hcl
# Example of the output structure I got
resource "aws_autoscaling_group" "example" {
health_check_type = "ELB"
health_check_grace_period = 300
# ... with commentary on why these settings align with AWS guidance
}
```
The biggest plus for me is the **connected understanding** across a whole document. For cloud ops, context is everything—a recommendation in chapter 3 might have a caveat in chapter 5. Claude seems to retain that linkage better on single, massive uploads.
Has anyone else made a similar comparison? I'm curious about your experiences, especially with technical or structured PDFs. Do you still use Humata for quick, chat-like queries on smaller docs?
~CloudOps
Infrastructure as code is the only way
I'm a cloud admin at a mid-sized fintech. We run our core infrastructure on AWS with Terraform, and I deal with a lot of AWS docs and compliance PDFs for audits.
**Context Window & Long PDFs:** Claude's 200k context is the real win. For a 100-page Terraform provider PDF, I can ask about a function mentioned on page 10 and a parameter detailed on page 95 in the same question. Humata often lost that thread. My tests show Claude can reference a detail from page 180 of a PDF if you ask directly.
**Output for Infrastructure Code:** Claude generates more directly usable Terraform/AWS CLI snippets. For the same query, Humata gave me a descriptive paragraph about security groups, but Claude gave me a full `aws_security_group` resource block with `ingress/egress` rules I could paste into a module. The commentary linking it to the PDF's best practices was a bonus.
**Cost & Limits:** Humata felt more "pay-as-you-go" via credits. Claude Pro is a flat $20/month. The trade-off is Claude's file upload has a 10-file/request limit and a 50MB max file size. I hit the file count limit once merging a bunch of short config guides. For one massive 40MB PDF, Claude worked; Humata would sometimes refuse it.
**Accuracy on Dense Tables:** Both can hallucinate numbers from complex tables. With AWS pricing tables in PDFs, I have to double-check. Claude seems to handle markdown tables extracted from PDFs slightly better, but it's not perfect. I would never trust a cost estimate from either without verifying.
I'd recommend Claude for your specific case of long, singular AWS/terraform PDFs. If you mostly work with dozens of smaller API docs under 20 pages each and want a credit system, stick with Humata. Tell us your average PDF count per session and your budget tolerance.
That 200k context window sounds great in theory, but I'm always suspicious of how Claude actually uses it. When you ask about a parameter on page 95, is it genuinely pulling from the tokenized text of that page, or is it just giving you a convincing synthesis based on a high-level understanding it built earlier? The outputs are often smooth, which makes it hard to tell.
Have you done any specific tests to verify the accuracy of the snippets it generates from the deeper pages? I've caught Claude inventing AWS property names that *sound* right but aren't actually in the PDF when you go back and check. That's the danger of a long context - you trust it more because it can "see" more, but hallucinations can still creep in.
And you haven't hit the file upload limits yet? I find they're pretty strict on the free tier, which makes testing with these massive docs a chore.
cg
The usable Terraform snippets are the only metric that matters. But you need to verify them.
That commentary linking to best practices is a potential hallucination vector. Claude's synthesis can blend the actual PDF with its general training. You must check the generated resource blocks against the source doc, line by line.
Have you actually pasted one of those `aws_security_group` blocks and run a `terraform validate`? That's the proof.
If it's not a retention curve, I don't care.
Great point about verification. I've found that Claude is referencing the text directly, but you're right that the smoothness can mask issues. I've tested by asking it to cite page numbers for specific statements. When I upload a security whitepaper and ask, "What does page 42 say about encryption key rotation?", it usually gets it right and even quotes the relevant line.
But you've nailed the real risk: > inventing AWS property names that *sound* right. I've seen this with IAM policy syntax. It once generated a `"Effect": "AllowDeny"` action - a convincing blend of the correct `"Allow"` and `"Deny"`, but totally wrong.
The free tier limits are a pain. I've had to split a 250-page compliance doc into two uploads. For deep testing, you almost need a paid account.
security by default