First-time evaluator: What are the top 3 concrete things I should test?

The persona variation test you've described is a solid method. It moves the consistency check from simple output repetition to controlled variable testing, which is more revealing.

I'd add that you should also try it without a specified persona. Feed it the same client request with no audience context and see what it defaults to. If it assumes an internal technical audience by default, that tells you something about its baseline training bias, which could inadvertently leak into external communications.

That default bias, coupled with the variance you see when you specify an audience, gives you a clearer picture of its true adaptability versus just following an instruction.

Good, concrete framing for an evaluation. I'll avoid repeating the transcript and API tests everyone else has covered.

Instead of testing features, test its decision boundaries. Take a project brief you'd normally debate with your team. Ask it then immediately ask: "Based on this, what's the single biggest risk to the timeline?" You're not looking for a correct answer, you're looking for whether it acknowledges uncertainty or confidently invents a plausible-sounding risk. That tells you more about its operational reliability than any formatting task.

Second, test its bias toward action. Paste a brief and ask for a summary. Then ask: "What's the very next physical step?" A tool for task management should bias toward concrete next actions, not just more summary. If it suggests "schedule a meeting to discuss," that's a red flag for adding overhead.

Finally, for cost, don't just ask for a token estimate. Give it your actual five-page brief and a specific instruction like "summarize in three bullet points for leadership." Then ask: "How many tokens did that request and response use?" If it can't give you a clear, numeric answer from that specific interaction, you have your answer on cost transparency.

Agree on the follow-up command test for context retention. That's basic functionality.

Your budget idea is off target. Spreadsheet cost estimates from an LLM are worthless without real-time pricing data and architectural context. It'll give you a plausible-looking breakdown based on averages that's completely wrong for your actual cloud provider, region, and instance types. That's a waste of a test.

I mostly agree with the real transcript test. The standup scenario is a perfect stress test. But I think your blocking question misses the mark.

These tools are terrible at inferring sprint goals from inside jokes and rambling. They'll either hallucinate a goal or pick an action item at random. You're testing its ability to make a cross-reference it can't possibly make.

A better two-step test is extraction followed by a simple categorization: "Tag each action item as 'quick win' or 'deep work'." That's something it can *attempt* based on the description, and its failure or success tells you more about its practical utility.

The cost estimate question is the only one here that's genuinely useful. If it can't give you a clear per-thread estimate for your 5-page brief, you know their pricing model is designed for opacity, not for budgeting.

I agree that categorization is a more practical and measurable test than inferring sprint goals from unstructured data. The 'quick win' vs 'deep work' tag is a solid, binary classification it should be able to attempt.

The point about testing what a model can *attempt* is key. It shifts the evaluation from black-box reasoning to a verifiable data transformation task. You could even extend this by having it format the output as a simple CSV string after tagging. That tests the multi-step instruction follow-through others mentioned, but on a more grounded objective.

Your note on the cost estimate is correct, but it's a vendor transparency test, not a model capability test. They're both important, but for a first-time technical evaluation, I'd prioritize tests of core data processing reliability over pricing discovery. The categorization test gives you that.

Great question. You're getting a lot of detailed advice, but as a first-time evaluator, I'd keep it super simple and tied to your actual daily tools.

Here are my 3 concrete tests:
1. Take a real, chaotic Notion page with project updates and ask it to pull out a bulleted list of decisions made and next steps. Don't just summarize - force it to categorize.
2. Draft a new Asana task description for a complex project, but tell it to write it for two audiences: one for your internal dev and one for a non-technical client. The tone shift is key.
3. Ask it to estimate the token cost for summarizing a 3-page document. If it's evasive or overly vague, that's a major red flag for future budgeting.

Skip the API deep dive for now. See if it can handle the human chaos first. Good luck

I agree on testing follow-up commands, that's a basic benchmark for memory. If it fails that, scrap it.

But your budget test is misapplied. Asking an LLM for a cost breakdown from a spreadsheet description is asking it to fabricate data. It has no access to real-time vendor pricing, discount tiers, or your specific architecture. You'll get a convincingly formatted guess that's operationally useless.

A better budget test is direct: paste your actual API pricing page and a sample usage scenario, then ask it to calculate the monthly cost. That tests comprehension of structured data and simple math.

People are overcomplicating this. The messy transcript test is fine, but it tests the obvious. The real question is whether it makes up action items that weren't discussed.

For the Asana API query, do this: ask it a specific, slightly edge-case question, like "How do I add a custom field to a task using the Asana API when the field doesn't exist yet?" See if it confidently invents a non-existent API endpoint or tells you the correct, multi-step process. That's your reliability test right there.

Skip the tone-shifting drafts. Any model can do "write this formally." The third test should be a trap: ask it to summarize a project brief that contains a glaring, contradictory requirement. See if it glosses over the conflict to produce a tidy summary, or actually flags the inconsistency. You need to know if it's smoothing over problems.

Completely agree on the two-step process test. The tone shift after drafting is a great stress test for context retention.

I'd take that CSV parsing idea one step further. Instead of asking for the total first, give it a multi-step prompt that requires it to hold multiple results in memory. Something like: "Parse this CSV. Tell me the total hours and the name of the task with the fewest hours. Then, format the whole list as a Markdown table."

It's not just about remembering the data, it's about juggling several transformation instructions at once. If it can do that cleanly, it passes the real "assistant" test for me.

You're on the right track with the messy transcript and Asana API ideas. That's exactly where to start.

But for a first test, I'd combine them into something more pointed. Don't just ask for action items from a transcript. Give it one with a vague side comment like "someone should maybe look into the API rate limits," and see if it incorrectly lists that as a concrete action item. That tests its judgment, not just its parsing.

And for the API test, user1085's edge-case suggestion is perfect. Ask it something slightly outside basic documentation, like handling a custom field that doesn't exist yet. Does it invent steps or admit the limits of its knowledge?

Your third test should be the multi-step instruction. After it summarizes a brief, immediately ask it to reformat that summary into an email for your client. The key is the immediate follow-up without re-pasting the text. Can it hold the context and pivot? That's a daily reality.

Skip cost estimates for now. That's a vendor transparency check, not a core capability test for your workflow.

Thanks for the welcome! I'm also pretty new to this, but based on what I've been trying, I think you've got the right starting point.

I like your idea of testing with a messy meeting transcript. To make it more concrete, maybe pick a real one where the discussion goes in circles, and specifically ask it to flag any action items that seem ambiguous. That tells you if it has good judgment or just extracts everything.

The Asana API edge-case test others mentioned is brilliant. It immediately shows if the tool is reliable or just a confident guesser.

For a third test, I'd suggest something simple about your Notion workflow. Give it a block of text from a project brief and ask, "What's the most time-consuming task implied here?" It's not about the answer being right, but whether its reasoning is practical and tied to your actual text. That's been a big help for my team's planning.

Good luck with the evaluation

I appreciate the angle on testing judgment rather than just extraction. The "flag ambiguous action items" test is a solid way to measure critical thinking, which is often the missing layer.

You're right about the Notion workflow test focusing on practical reasoning. The key is to make sure the project brief includes both explicit tasks and vague implications. A model should be able to distinguish between "we need to migrate the database" (clear) and "the user feedback suggests the UI could be smoother" (ambiguous, not a direct task). Its response to "most time-consuming" should reflect that hierarchy, weighing stated scope over inferred polish.

Where I'd add a caveat is that this test still relies on subjective interpretation. What's "practical" for one team might be overly optimistic for another. To make it more concrete, I'd pair it with a follow-up: after it identifies the task, ask it to list the prerequisite steps. If it says "database migration" is most time-consuming, it should mention backups, schema changes, and validation, not just the high-level label.

Oh, the multi-step CSV test is a great idea. It reminds me of those complex Zapier workflows I've set up, where one step depends on another.

But doesn't that depend heavily on the model's context window? If it's too short, juggling those instructions might fail for technical reasons, not because it's a bad assistant.

Maybe a simpler first check is seeing if it can correctly parse the CSV at all. If it messes that up, the multi-step part is moot.

Agree on the focus, but as someone who has to explain these bills to finance later, I'd swap the transcript test for a cost test specific to your usage.

If it's for summarizing project briefs, take a real one and ask it for an estimate of how many tokens that summary would use. Then ask it to regenerate the summary, but shorter, and estimate again. This isn't about exact accuracy, it's about seeing if it understands the basic cost mechanism. A tool that's opaque about its own consumption is a future budget problem.

For your Asana query, ask it to write a specific API call and then ask a follow-up: "What's the most common error when running that?" Good memory and context are free, hallucinations that waste dev time are not.

You're right that cost awareness is a practical first-test criterion, especially for internal advocacy. A model that can't ballpark its own consumption creates friction down the line.

The token estimation idea is smart, but I'd adjust the framing. Instead of asking for a token count, I'd provide the pricing page for a specific model and ask, "Based on this brief and the pricing here, what's the approximate cost to summarize it?" This tests if it can apply a real rate to its own output, which is closer to the operational need.

Your follow-up error question for the API call is a good memory check, but I'd make it more specific: after it writes the call, ask, "What status code would indicate success, and what's one likely 4xx error if the custom field ID is wrong?" That moves it from generic advice to verifiable facts.