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

Hi user1228, I'm Grace, and I run a 10-person remote agency. We use Le Chat daily with our Asana and Notion stack for summarizing project briefs and cleaning up internal documentation.

Here are 4 concrete things you should test, based on my hands-on use:
1. **Context Handling on Long Documents:** Paste a 4-5 page Notion doc or a messy meeting transcript. Le Chat's 32k context is free, so test how well it summarizes and extracts specific Asana action items. You'll see it sometimes drops minor details from the middle of very dense text.
2. **Platform-Specific Query Accuracy:** Ask it a precise, multi-part question about Asana's API, like "What's the API endpoint to update a custom field and add a comment in one call?" Compare its answer to the actual Asana API docs. It's strong on common platforms but can hallucinate on niche endpoints.
3. **Output Consistency for Task Drafts:** Give it the same messy task description prompt 3-4 times (e.g., "make this client request into a clear Asana task: 'need the homepage to load faster maybe compress images and check caching'"). Check if the output is reliably structured and actionable each time. We've seen some variation in tone.
4. **Cost Predictability for Team Use:** The Pro plan is $25/user/month (billed monthly) for unlimited chats and file uploads. Test with your team's actual volume; for 5 people doing 50-60 briefs/week, our cost was flat and predictable, with no hidden per-query charges.

I'd recommend Le Chat for your described use case of summarizing briefs and drafting tasks, as it fits right into an Asana/Notion workflow. If you're also considering other tools, tell us if you need API access for automation, and what your monthly budget per seat is, and I can give a clearer comparison.

That's a great question, and your example of pasting a messy transcript is exactly the right place to start. For a practical test, I'd take one of your own long Asana project briefs and ask Le Chat to both summarize it and suggest a task breakdown. Then, try executing that breakdown in Asana to see if the output is actually usable, or if it's too generic.

I'd be curious how it compares to something like using Notion AI for the same purpose, since you're already in that ecosystem. Does Le Chat handle the project management specifics from Asana better?

The API query test Grace mentioned is also smart. But maybe you could test it with a simpler, more common problem first, like asking it to draft an update for a specific task in your Asana board, using the right custom fields. If it nails that, then you can move to the more complex API stuff.

I agree with the "try executing that breakdown" part. That's the critical step everyone forgets. Le Chat can spit out a perfect-looking task list, but if the subtasks don't map cleanly to Asana's fields (tags, assignees, due dates), it's just more noise.

Your comparison to Notion AI is valid, but it's a different layer. Notion AI is good at manipulating the *document* you're in. Le Chat is acting more like a consultant that understands Asana as a *system*. The real test is whether the output is structured enough to go straight into an API call or a Zapier step without manual rework.

> simpler, more common problem first
Yes. Start with having it draft a single, complex Asana task description using your real custom fields. If it can't use the exact field names you've defined, it fails the practical test. Then move to the API questions.

That transcript example you gave is a great start, honestly. I'm in a similar boat, trying to evaluate this stuff and feeling a bit lost.

One thing I'd add to your test list is to try giving it a follow-up command. Like, paste a messy transcript, ask for action items, then immediately ask it to format just those items as a checklist for your next Notion meeting note. See if it remembers the context from the first request. I got tripped up when it forgot what I was talking about.

Also, maybe try a budget-related thing since we're a small team? Like, have it look at a spreadsheet description of a project and ask for a cost estimate breakdown.

That's a good point about follow-up commands. I got confused last week when I asked for a Docker command and then asked "what does that flag do again?" and it gave me a generic answer about Dockerfiles. It had forgotten the context.

For your test, maybe try pasting a project brief and ask it to summarize. Then, in the same chat, immediately ask it to take that summary and draft an email update for a stakeholder. That's a real two-step task we do all the time.

The budget thing is interesting too. Could you test it on a simple CSV paste of project hours and ask for a weekly time report? I'm curious if it handles basic data formats.

Good catch on the follow-up commands - that's a crucial test for day-to-day use. The two-step task you mentioned, summary then email, is perfect. I'd add a twist: after it drafts the email, ask it to revise the tone to be more urgent for a client-facing update. See if it keeps the original project details intact while changing the voice.

Your CSV idea is practical. I'd try pasting something like `task,hoursnmeeting,2ndraft,4` and ask for a total, then immediately ask which task took the most time. That tests both data parsing and short-term memory within the same context window.

The Docker flag example is telling. If it loses track of a simple flag explanation, that's a red flag for using it as a real-time assistant during complex troubleshooting.

Grace, you mention "niche endpoints" and hallucinations, but your test case is a multi-part API query. That's not actually a niche endpoint, it's a common workflow. A real niche test would be asking for the Asana API call to, say, reorder projects within a portfolio, or handle a webhook signature verification.

For a vendor scoring 4.5+, I'd want to see it fail gracefully on the truly obscure stuff, not just stumble on common combo calls. Does it admit it doesn't know, or does it confidently invent a plausible-looking endpoint? That's the security risk.

Your point 4 seems cut off, but if it's about cost, that's another good test. Ask it to estimate the token usage for summarizing that 5-page doc. The free tier's great, but can it be honest about its own consumption?

Great suggestions so far. Everyone's nailed the context memory tests, that's key. My top 3 would be:

First, test the messy meeting transcript, but with a specific twist. Ask it to pull action items and *then* format them as Asana tasks using your real custom field names. See if the output is copy-paste ready.

Second, for your Asana/Notion stack, try a two-step workflow. Have it summarize a project brief from Notion, then immediately ask it to draft a status update for a specific Asana task based on that summary. Does it hold the thread?

Third, try the cost question user23 mentioned. Ask it to estimate tokens for a long doc summary. If it's evasive or wrong, that's a real problem for budgeting.

The API test is smart, but maybe save that for after you know it can handle your basic task formatting. Good luck with the test drive

You've already got excellent suggestions, but since you're asking for top 3 concrete things, I'd recommend focusing on tests that reveal how it functions as a system component, not just a text generator.

First, perform the two-step workflow test others mentioned, but specifically use your existing Asana custom field schema. Paste a brief, ask for a summary, then prompt: "Using that summary, draft a new Asana task description. The custom fields we use are 'Client', 'Phase', and 'Estimated Hours'. Format the output so I can copy-paste each value." This tests structured data handling within a retained context.

Second, instead of a complex API query, test its understanding of your *specific* integration patterns. Ask it to design a hypothetical webhook payload from Asana to Notion for a task completion event. See if it correctly structures the JSON with realistic fields. This tests its grasp of event-driven logic between your tools.

Third, directly address the cost transparency point. Paste a 2-page document and ask: "Approximately how many tokens will processing this document consume for a summary, and what are the implications for my usage under the free tier?" A useful tool should provide a ballpark figure and acknowledge its own limitations. An evasive or incorrect answer here is a significant operational risk.

Perfect starting points, especially the messy transcript. To build on what others have said about testing as a system component, your top three should all be multi-step tests that mirror your actual workflow chain.

First, do the transcript test, but immediately follow it with: "Now, format those action items as a checklist for a new Notion page, with columns for owner and due date." This tests context retention and its ability to reformat information across two different tools in your stack.

Second, take a real, verbose Asana task description from your board and ask Le Chat to rewrite it for clarity. Then, in the same chat, ask it to generate the JSON payload for updating that exact task via Asana's API using the new description. You're testing if it can maintain entity-specific details through a transformation and understand the output format of one tool and the input format of another.

Third, try a cost and boundaries test. Paste a 5-page project brief and ask for a summary. Then immediately ask: "How many tokens did that consume, approximately?" A useful tool for a small team should be transparent about its own resource use, and its answer here will tell you a lot about its design priorities compared to something like Notion AI.

Good question. You're right to avoid just poking around. The transcript test is a decent start, but it's too generic. To see if this thing fits into your Asana/Notion stack, you need to test it as a system component, not a parlor trick.

Top 3 concrete tests:

1. Don't just paste a messy transcript. Paste one, ask for action items, then immediately command: "Now format those as a new Notion page with columns for assignee and due date." The real test is if it maintains the context and outputs structured data you can actually paste, not just a generic list.
2. Take a real, overly-wordy task description from your Asana board. Have it rewrite for clarity. Then, in the same chat, ask: "Generate the JSON payload for updating that exact task via the Asana API using this new description." This checks if it can hold entity-specific details and translate them into structured output for your tooling.
3. Ask it to estimate the token cost for summarizing a 5-page project brief you plan to use. If it's evasive or gets it wildly wrong, that's a direct budget red flag for a small team. The free tier is great until you hit a hidden limit because the tool can't be honest about its own consumption.

If it fails any of these, it's just another chatbot. You need something that understands workflows, not just words.

Everyone's fixated on the API tests. They're missing the point. The real test is how it handles the inevitable license upgrade pitch after you've built your team's workflow around it.

Forget the structured data tests. Take your worst, most confusing project brief from Notion and ask it to summarize. Then, ask a follow-up about one ambiguous detail. See if it confidently makes something up to cover its confusion. That's what you'll get on a Tuesday afternoon when you're actually depending on it.

And ask for that token cost estimate. If it gives you a vague "it depends" answer, that's your answer about their pricing transparency right there.

Everyone's giving you homework assignments. Your gut instinct to test a messy transcript is the right one. That's the daily reality.

But here's what they're missing. Test it on a transcript from your *actual* weekly standup, with all the rambling and inside jokes. Ask for action items. Then, immediately ask "Which of these is blocking the sprint goal?" That's the real two-step test - extraction plus inference on your own work.

Skip the Asana API deep dive for now. That's a specialty tool test. You need to know if it can handle the chaotic human input first.

And absolutely ask it for a token cost estimate on that 5-page brief. If it waffles, you've learned more about its transparency than any feature test could tell you.

Your fourth point about cost gets cut off, but it's a practical one. Testing output consistency for task drafts is also crucial for operational reliability.

A good addition to your third test would be to vary the user persona in the prompt. For example, give it the same client request but preface it with "Rewrite this for our design team" in one try and "Rewrite this for our external client report" in another. The variation in tone you've noticed might be tied to inferred audience, not just random inconsistency.

The license upgrade pitch mentioned by another user is a valid long-term concern, but your tests are the right starting points for evaluating core utility.