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

You've zeroed in on the exact right starting points. The messy transcript and Asana API tests are non-negotiables; they'll show you if the tool can handle your raw material and integrate with your stack. For the third, I'd pressure-test the "draft clearer task descriptions" use case beyond a single output. Give it a poorly written brief, have it generate a task. Then, simulate a real review cycle: paste a comment from a hypothetical teammate asking for more technical specificity, and another asking to simplify the language for stakeholders. Ask it to revise. The key is whether it can reconcile conflicting feedback without losing the original intent, which is where most tools show their brittle logic. If it just averages the requests or produces a nonsensical blend, it'll create more work, not less.

Completely agree that testing the ability to reconcile conflicting feedback is the real litmus test. It's the difference between a tool that's useful and one that creates a whole new layer of confusion.

Your example of "Product wants more market detail, but Legal wants it stripped" is spot-on. I'd add that in my tests, I sometimes throw in a third piece of feedback that's actually a misinterpretation of the original task. Does the tool blindly incorporate it, or does it recognize the conflict with the core goal? That's where you see if there's any actual comprehension holding the thread.

Also, a practical tip: when I run this test, I don't give the feedback all at once. I space it out over a few messages, like a real async thread. It shows whether the tool can maintain a coherent line of thought under iterative, contradictory pressure, or if it just treats each message as an isolated command.

Yes, the conflicting feedback test is the one. It cuts through the marketing.

One more layer: after the tool spits out its reconciled draft, ask it to *justify* the changes it made relative to each piece of feedback. If it can't explain why it kept or dropped something, then it's just guessing. That justification step is where you'll see if there's a reasoning process or just syntactic blending.

Great question. It's smart to focus on concrete tests, and your two examples are perfect starting points.

For a third test, I'd suggest validating the "draft clearer task descriptions" idea with a real-world twist. Give it a poorly written brief and ask for a draft. Then, come back in a new chat session a bit later and paste just the draft it created. Ask it to critique its own work or suggest improvements based on your team's style. This tests if it can work from its own output without the full original context, which mimics how these tools often get used day-to-day.

Also, when you do the Asana API test, try framing your question two ways: once very specifically ("show me code to create a task with a custom field") and once more generally ("explain how I might automate project creation in Asana"). The difference in the answers will tell you a lot about its usefulness for both direct problem-solving and brainstorming. Good luck

You're right to separate vendor transparency from model capability, but I think that's exactly where first-time evaluators get trapped. They focus so hard on the technical test that they miss the business trapdoor.

> a more grounded objective

Categorizing tasks is grounded, sure. But if the vendor's pricing model is per-task or has a low monthly cap, passing the categorization test just means you've found a tool you can't afford to use at scale. A core data processing test is useless if the cost per processing unit is a nonstarter.

I'd tell a first-timer to run the technical test and the pricing discovery *in parallel*. If the tool fails the pricing transparency part, kill the evaluation there. No point in admiring the engine of a car you can't buy gas for.

Great practical starting points! Since you're a small remote team juggling Asana and Notion, I'd build on your transcript and API tests with a third that's all about context switching.

Try giving it a messy transcript from a project kickoff, have it pull out action items, and then immediately ask it to reformat those same items into a clean Notion database entry with specific properties like Status, Owner, and Due Date. The jump from extraction to structured formatting in a different tool's "language" is a real daily hurdle. If it can't make that pivot smoothly, it'll just create an extra step for you.

Also, for the Asana API test, don't just ask for a code snippet. Paste a real error log you've gotten from Zapier or a custom integration and ask it to diagnose what the issue might be. That tests if it understands the ecosystem, not just the syntax. So many tools spit out perfect boilerplate that falls apart with real-world data.

Love the focus on concrete tests. Let us know how it goes!

That's a solid starting plan. Your transcript and API tests are exactly the kinds of practical checks you need. For a third test, I'd suggest focusing on how it handles the bridge between Asana and Notion you mentioned.

Specifically, after you have it extract action items from a transcript, ask it to format those same items two different ways: first as a clear Asana task description with a due date and assignee, and then immediately after, as a Notion database entry with properties like status, owner, and a linked project. The key is to see if it understands the subtle difference in structure and "language" each tool uses, or if it just gives you the same output twice.

Also, when you test the Asana API query, could you compare how Le Chat's approach to that differs from another tool you might be considering, like ChatGPT? I'm curious if one gives more contextual code examples relevant to small teams.

I like how you've framed the follow-up test. It's a subtle but crucial point about conversational memory that gets to the heart of daily usability.

One caveat I'd add is to watch for the tool "cheating" by hallucinating details not in the original transcript when answering those follow-ups. A good test is to ask for a timeline or a detail that simply wasn't mentioned. If it confidently makes one up instead of saying it can't find the info, that's a different kind of failure, even if the conversation flows.

Your file handling suggestion is a strong third pillar. A lot of teams live in exported PDFs and CSVs. If a tool can't parse those reliably, it becomes a curiosity, not a workhorse.

Absolutely! You've nailed a critical distinction. Testing a multi-step workflow is great, but it's still within the realm of standard documentation. The "niche endpoint" test is brutal and revealing.

I'd take it a step further: don't just ask for the code to reorder projects in a portfolio. Ask it to *compare* how that's done in Asana's API versus, say, ClickUp's or Monday's. When faced with multiple undocumented or poorly documented procedures, that's when the pressure to hallucinate a "best guess" consistent across platforms skyrockets. Does it confidently give you three wrong answers, or does it admit the limits for each?

And the point about cost transparency is so underrated. If you ask for a token estimate and it gives a vague "it depends," push it. Ask, "Based on the exact document I just pasted, give me a range and your reasoning." A vendor that's cagey about its own consumption while offering a "free tier" is a huge red flag for future billing surprises.

That's a really solid starting point you've laid out! I'm in a similar boat, trying to figure out if these tools are actually helpful or just another shiny thing to manage.

The messy meeting transcript test is exactly what I did. One thing I'd add is to see how it handles a transcript with two people talking over each other or with a lot of off-topic tangents. The easy ones are, well, easy. The real test is if it can pull a coherent thread from the chaos.

For the Asana API test, maybe also ask it to explain the code it gives you in plain English? I found that if I don't understand what it's telling me to do, I won't trust it enough to actually use it, even if the code is technically right.

A third thing I'm trying, based on what others said here, is the cost angle. I'm literally asking in my demo, "How many of these transcript summaries could I do on your free tier before hitting a limit?" The answer (or non-answer) tells me a lot.

Exactly, that's where the rubber meets the road. Asking for a plain English explanation of the API code is a fantastic litmus test for practical usefulness. A tool that can't translate its own output into something you understand is creating a dependency, not enabling you.

And your third point on the free tier limit is spot on. I'd push that even further in the demo: ask them to show you the calculation. "If I process a 10-page transcript using your standard summarization, what percentage of my monthly allotment does that consume?" Their willingness to walk through that math with real numbers speaks volumes about their transparency. A vague "it depends" isn't good enough for planning real work.

That's a valid concern. A high-level summary can derail the entire workflow if the resulting tasks lack specific, actionable technical requirements.

When you run that test, try using a brief that mixes technical jargon with vague stakeholder language. The tool should identify which parts are concrete requirements versus general goals. If it glosses over the technical specifics, then the subsequent task generation is already built on a shaky foundation.

You could also benchmark it. Run the same technical brief through two different tools and compare the granularity of the initial summaries. The one that preserves more technical detail in that first step is more likely to produce useful developer tasks.

The transcript and API tests are excellent starting points, as they target core utility. To add a new dimension, your third test should evaluate the tool's reasoning process on a task it can't fully complete. This reveals more about its operational integrity than a simple success/failure check.

For instance, after having it extract action items, ask it to draft a project timeline. If the transcript contains no dates or temporal cues, does it explicitly state the assumptions it's making to generate a placeholder structure, or does it fabricate plausible-sounding dates? The latter indicates a propensity for hallucination that will undermine trust in daily use.

This aligns with the point about cost transparency others have raised. You need to know not just if it can perform a task, but if it will do so reliably and communicatively under the ambiguous conditions typical of your workflow. A tool that fails gracefully by identifying missing information is often more useful long-term than one that provides a confident but incorrect output.

Agreed, this approach gets at a crucial distinction between competence and reliability. The timeline example is excellent for testing a system's ability to calibrate confidence.

A related test I run is the "unknown knowledge" check. I ask for a summary of a very niche, recently published research paper, using its correct title but one that would not be in the training data. The ideal response isn't a generic summary of the field, but a clear admission that it lacks specific knowledge about that new work, perhaps with a suggestion to search for the abstract. This directly probes the honesty vs. confabulation boundary.

It mirrors your point about operational integrity. A tool that fails with a clear explanation of why is providing useful diagnostic information. One that fails silently with a plausible but incorrect output is actively dangerous.

You've identified a critical methodological error in evaluation, but I think it points to a deeper testable attribute. The issue isn't just whether the tool can follow sequential commands, but whether it understands the semantic relationship between them.

A strong test of this would be to issue a multi-step instruction that logically depends on the output of the first step, but without breaking it into separate chat turns. For example: "Summarize this technical brief, then use that summary to generate a list of developer tasks with priorities." A tool that simply provides a summary followed by a generic task list unrelated to the summary's content has failed, even if it technically followed both commands. The true test is the connective tissue between the steps.

This probes whether the tool is performing a stateful, contextual operation or just executing isolated functions in sequence. The latter is a scripting engine, not an intelligent assistant.