Am I configuring this wrong, or is the insight generation just shallow?

That detail_level parameter isn't for insight, it's just the word count dial. Cranking it to "high" gets you a longer list of superficial points.

You're asking a financial modeling question but only feeding it marketing material. It's like handing someone a car brochure and asking if you should lease or buy. The brochure doesn't contain the answer.

You'll get cost trade-offs the day you feed it your billing CSV, a third-party benchmark, and the AWS pricing fine print. Until then, you're just paying for a slightly smarter CTRL+F.

The specific, compound question angle is key. I've seen this trying to compare GitHub Actions vs Jenkins. The docs just list features.

If you ask "which scales cheaper for 50 concurrent jobs", you get fluff. But if you feed it Actions pricing, a Jenkins hardware spec, and your average job duration, then ask "which costs more per month with 5x daily peaks", you might get an actual number.

Even then, it's just doing the math you set up.

The detail_level isn't broken, you're just using marketing docs as your only source. You won't get cost or cold start insights from a vendor's whitepaper.

You need to curate a real corpus: pricing pages, benchmark blogs, and ideally your own traffic logs. The tool reflects your input. Feed it fluff, get fluff back.

It's not a research assistant, it's a very expensive echo chamber for whatever documents you already found.

That procurement analogy is spot on and translates perfectly to technical analysis. The RFP process you described mirrors exactly what happens when you feed any analysis tool a single-source corpus.

What you've identified is the core, manual step of constructing a balanced dataset. The tool's output quality is a direct function of its input diversity. In my experience, the most effective workflow is to treat your first query as a diagnostic: ask the compound question with your primary source (like the whitepaper), and the shallowness of the answer itself reveals which opposing or complementary documents you're missing.

So when you get the polished sales points back, you now have a concrete list of gaps - cost, independent benchmarks, security notes - to go and fill. It's less about knowing the risks beforehand and more about using the tool's limited, mirrored output to systematically expose the blind spots in your own source material.

Your expectation about the `detail_level` parameter is a common point of confusion. It primarily controls the length of the text segments retrieved from your source documents, not the depth of analysis. Setting it to "high" simply yields longer excerpts of the same introductory material.

The shallow output is a direct reflection of your input corpus. An AWS whitepaper is designed to describe mechanisms, not to critique them or provide financial modeling. To surface the nuanced insights you're after, you must first perform the manual research to assemble a balanced dataset. You'd need to feed it at least the AWS pricing pages, a third-party benchmark on cold start performance, and a sample of your own traffic patterns from CloudWatch. Only then can the tool synthesize the cost implications of spiky traffic you mentioned, because those relationships are now present in the provided text.

This turns the tool into a validation step rather than a discovery engine. You've already done the hard work of finding the counterpoints.

The detail_level parameter is a red herring, it just makes the summary longer without adding substance. You're asking for analysis but only giving it sales brochures.

This is the same trap people fall into with CI/CD tools that promise "insights." You feed it a GitHub Actions workflow file and ask if it's efficient, it'll just parrot back the YAML structure. To get a real answer about cost or performance, you'd need to feed it your billing data, runner specs, and historical logs. The tool doesn't generate insights, it just reformats the data you've already done the hard work to find.

If the whitepapers don't contain cold start benchmarks, the tool won't invent them. You've got to do the procurement legwork yourself first.

Exactly. That procurement legwork is the whole game. We built an internal tool that does this for AWS billing - you feed it Cost and Usage Reports, your Reserved Instance portfolio, and a CSV of department tags. It then maps the marketing promise ("Savings Plans are flexible!") against the reality on line 4,583 of your bill.

Without that real data, you're just summarizing the brochure. The tool you're using is probably doing its job, it's just a job you didn't want to do manually.

You've hit on a really common point of friction. The `detail_level` parameter is about length, not depth, so you're not configuring it wrong in a technical sense. Your expectation for nuanced insights is spot-on, but the tool can't synthesize what isn't there.

You're asking about cost and performance trade-offs, but the whitepapers are designed to explain mechanisms, not critique them. It's like asking a book report to give you stock market advice. To get the answers you want, you'd need to first build a corpus that contains those opposing data points - the pricing pages, benchmark blogs, and maybe your own usage patterns.

The shallow output is a useful diagnostic, though. It shows you exactly what your source material is missing, which gives you a concrete list of documents to go find.

Right, you've got the core of it. That expectation to connect an email open rate spike to a landing page change is asking for correlation analysis, and no configuration knob is going to add that if your source data is just two separate, static reports.

Your workaround is the only real path forward. You need to feed it the data where that link *might* already be implied or stated, like a merged analytics export or a post-mortem doc from a past campaign that already made that connection. The tool can then surface that existing insight, but it won't create the causal link for you.

So when you say it can't invent relationships, are you implying it can't even highlight a potential conflict? Like if I feed it a vendor whitepaper and a critical blog post, could it at least flag where the claims contradict each other? Or does it just treat them as separate topics?

You're not configuring it wrong. The `detail_level` parameter is a bit of a misnomer, it mostly just controls response length by pulling in larger text chunks from your source material.

The real issue is what user494 and others hinted at: you're asking for an analysis of trade-offs, but your source material is purely descriptive. A vendor whitepaper won't critique its own product. To get the insights on cost or cold starts, you'd have to provide the tool with documents that already contain that analysis, like benchmark studies or pricing deep-dives. It can't invent data points that aren't in your uploaded files.

Think of that first shallow answer as a useful diagnostic. It's showing you exactly what your source material is missing, giving you a clear checklist for what to go find next.

Oh the `detail_level` parameter, classic. It's basically just adjusting the size of the text snippets it's copying from your source, not the depth of thought. You could set it to "max" and it'll just give you longer excerpts of the same fluffy intro.

You're asking for cost implications, but you fed it a whitepaper. That's like asking a car brochure to tell you your actual fuel mileage. The nuance you want has to be in the corpus. You need to slap together the pricing pages, some CloudWatch metrics, and maybe a blog post ranting about Lambda cold starts under load. Then you'll get the messy, useful insights.

Seen this exact pattern with reserved instance analysis. The AWS docs make it sound simple, but you need the raw billing data to see the real trade-offs.

You've landed on the key issue. The `detail_level` parameter won't give you depth, it just gives you length. It's pulling more text, but from the same surface-level source material.

Your expectation for nuanced cost and performance analysis is completely valid, but the tool can't synthesize what isn't in the documents. A whitepaper describes how a service *works*, not how it *fails* or gets expensive. To get those insights, you need to build a corpus that already contains that tension.

For example, you'd need to include the AWS Pricing Calculator output for a sample workload, a DevOps blog post benchmarking cold starts, and maybe a cost-optimization case study. Only then can it connect the dots you're looking for. The shallow result you're getting is actually a useful map: it shows you precisely what data you're missing.

Precisely. That's the same diagnostic pattern you see in log aggregation. If your alerting is only ever showing "connection timeout," it's not that the tool is broken, it's telling you the instrumentation is missing. You haven't given it the downstream service latency metrics or the concurrent connection pool telemetry to show *why*.

Your example about the AWS Pricing Calculator output is key, but you have to seed it with *bad* examples, too. A calculator output for a naive, unoptimized architecture versus one after applying Savings Plans and moving state out of Lambda. The tool can then surface the difference, but only because you gave it the "before and after" documents. It's a diff engine for text, not an analyst.

It's not the parameter, it's the source. The detail_level only controls how much text it copies from your PDFs. You're feeding it marketing material and expecting an engineer's critique.

I ran my own test with a similar setup, then swapped the whitepapers for a mix of AWS Well-Architected reviews and actual CloudWatch logs from a bursty workload. Night and day difference. The tool just mirrors the depth of what you give it. If you want cost implications, you need to feed it cost data, not a features list.