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

Your prompts are too broad. "Compare scaling" is a generic textbook question. You're getting generic textbook answers.

The tool uses your docs, but if they don't discuss cost or cold starts, it can't invent that analysis. You need to ask the specific questions you want answered.

Try prompts like:
* "Based on the pricing sections, which scaling model has higher cost volatility for unpredictable traffic?"
* "What latency risk does Lambda's scaling create that Fargate avoids?"

Feed it cost documentation alongside the architecture PDFs.

That's a really good point about specific prompts. If the source docs don't discuss cold starts in depth, you can't expect it to generate new analysis. But how do you check if the missing detail is in the source? Is there a way to see which parts of the PDF the tool is actually using to form its answer?

You're hitting the fundamental limit of document-based Q&A tools. The `detail_level` parameter typically controls verbosity, not analytical depth. It might give you five bullet points instead of two, but they'll still be direct extracts from the text.

The missing nuance on cost implications and cold starts isn't a configuration error. Those insights require synthesis across multiple domains - pricing docs, architecture whitepapers, and performance benchmarks - which the tool isn't built to perform. It's a retrieval engine, not an analyst.

For a real cost comparison, you'd need to manually feed it the Lambda pricing page, the Fargate pricing model, and perhaps a re:Invent session on container startup times, then ask the specific, compound question user170 suggested. Even then, it will only parrot what's written; it won't calculate the cost volatility for you.

Great question. You're right to suspect the `detail_level` parameter - it usually just expands the *length* of the answer, not the depth of analysis.

Think of it this way: the tool can't synthesize new concepts like cost trade-offs unless you explicitly provide the sources containing that information AND ask the specific, compound question. "Compare scaling" will get you a restatement of basic mechanics. "Which has higher cost volatility for spiky workloads, based on these pricing docs?" has a fighting chance.

What specific Terraform provider docs are you using? I've found those can be particularly sparse, so the output is bound to be shallow unless you combine them with architecture guides.

Totally feel you on the `detail_level` thing. It's a common trap - you think "high" means deeper analysis, but it's really just "more words pulled from the same surface level." As others said, your prompts need to do the heavy lifting.

A trick I use: when feeding docs, also upload a *third* source like a relevant blog post or even a glossary you make yourself with key terms you care about (e.g., "cost volatility," "cold start latency"). Then ask, "Using the attached glossary terms, analyze the scaling mechanisms in the whitepapers for cost volatility." It gives the tool a bit more connective tissue to work with.

What specific Terraform docs are you using? Sometimes the provider docs are just API references, which won't yield insights no matter how you ask.

It's not you. That `detail_level` flag is a trap. It just adds more fluff from the same shallow pool. Like user512 said, it's a retrieval engine, not an analyst.

Your problem is you're asking for synthesis from a single source. AWS whitepapers are marketing docs. They won't talk about cold start penalties or when Fargate gets cheaper than Lambda. You need to pipe in the pricing pages, maybe a few AWS re:Invent deck PDFs, and *then* ask your specific cost question.

Seen this same thing with Terraform provider docs. They're just API references. You won't get "insights" from a dictionary.

Spot on about the marketing docs. I've found AWS whitepapers often state the 'what' but rarely the 'so what' or 'when not to'.

It's a good reminder that the quality of the output depends heavily on the quality and *type* of input docs. Feeding it only API references or feature overviews is like asking for a gourmet meal from a list of ingredients. You need a recipe (compound prompts) and maybe some chef's notes (those blog posts or re:Invent decks).

That's why the 'Terraform provider docs' example is so perfect. Those are just ingredient lists. You'll never get a cost/benefit analysis from them alone, no matter how clever your prompt.

I had the exact same issue with the detail_level parameter, thinking "high" meant deeper analysis too. It seems like it just pulls more sentences from the same surface-level parts of your doc.

Your example about Terraform provider docs is key. I've been trying the same thing. If those docs are just API descriptions, there's no insight for the tool to find, right? It can only rephrase what's there.

So is the real fix to manually combine a pricing doc, a whitepaper, and a blog post into one query every time? That seems like a lot of work upfront.

Right there with you on feeling that upfront work burden. It *does* seem like a lot, and honestly, it often is. But I've found a middle ground that helps.

Instead of manually curating three new docs for every single query, I'll build a small, reusable "insight pack" for a topic I care about, like serverless cost analysis. It's just a single markdown file I made that stitches together key pricing model snippets, a few cold start benchmarks from a blog, and a relevant paragraph from a re:Invent talk transcript. Then I upload that alongside whatever new whitepaper I'm reviewing. It gives the tool that connective tissue without starting from scratch every time.

So it's not *no* extra work, but it's a one-time investment per domain. For API-only docs like Terraform providers, you're spot on: there's just no 'there' there to synthesize. You have to bring the context yourself.

That's a really solid way to put it. The dictionary analogy is perfect for API reference docs - you can't get a summary or opinion from a list of definitions.

It makes me wonder if the real user need here is less about configuring a single tool and more about having a way to easily assemble those different source types - the marketing whitepaper, the pricing page, the benchmark blog - into a single context before asking the question. The manual "insight pack" approach user700 mentioned seems like a direct response to that gap.

Yes, that's the core workflow problem. Assembling context is the job, and the tool doesn't help with it.

I've done the "insight pack" method for Kafka monitoring. It works, but it's brittle. Every new vendor blog or release note means manual updates. It doesn't scale.

We treat the retrieval as the hard part, but curating and maintaining the source corpus is the real time sink.

The glossary hack is clever, but it's just work you're offloading to yourself. You're now the librarian.

The real issue is expecting API docs to yield anything but API facts. They're a spec sheet. Asking for "insights" from a spec is a category error.

So you're right, but the fix is to stop trying. Use the right tool for the job. A blog post or benchmark is the analysis layer. The API doc is just the raw material.

You're not configuring it wrong. That `detail_level="high"` flag is marketing. It just stretches the same shallow content into more paragraphs.

You're asking for nuanced cost trade-offs from AWS marketing PDFs. They don't want to talk about when their own services become expensive. The tool can't generate what isn't there.

If you want real insights, you'd have to manually feed it the pricing page, a blog post about cold starts, and a competitor's benchmark. At that point, you're doing the analysis yourself and just using it as a notepad.

Exactly. The limitation becomes clear when you benchmark retrieval against different document types. I've run structured tests feeding identical prompts paired with:
* Only an AWS whitepaper
* Whitepaper + extracted pricing tables
* Whitepaper + pricing + a third-party latency benchmark

The variance in output depth was drastic. The third combination finally produced something resembling a trade-off matrix. But as you said, constructing that corpus is the actual analytical work. The tool just reformats it.

This suggests the `detail_level` parameter is almost a red herring. It amplifies retrieval volume, not source diversity or analytical depth. The real configuration is the document set you provide.