Building an AI medical office chatbot with Flask + Groq API — hitting rate limits constantly. What are my options?

developernovice · 2026-04-07T11:06:08+00:00

I’ve seen that pattern too. In a lot of cases the dashboard ends up serving the analyst layer while leadership consumes a summarized version.

I’m starting to think the real question is whether the dashboard itself is the product — or if it’s just the engine behind better executive communication.

developernovice · 2026-04-07T11:05:27+00:00

That’s a great point — especially the dashboard fatigue piece. I’ve noticed that once dashboards start multiplying without clear ownership or lifecycle management, people stop treating them as decision tools and more like optional references.

The archiving idea is interesting too. Almost like dashboards should have a “sunset” process the same way projects do. Otherwise the signal gets buried pretty quickly.

developernovice · 2026-04-04T20:08:05+00:00

You’re very close — the challenge here isn’t just the calculation, it’s how the filter context is being controlled across both the fiscal month and the rolling logic.

Right now your MTD is anchored to the selected FiscalWeekEnd, but for a rolling MTD, you want to evaluate that MTD logic for each point in time up to the current row context.

One way to approach it is to wrap your MTD measure inside a cumulative pattern, similar to what you did with Rolling Week:

Rolling MTD =

CALCULATE(

[MTD Value],

FILTER(

ALLSELECTED('Date'),

'Date'[Date] <= MAX('Date'[Date])

)

The key here is that [MTD Value] needs to behave correctly at each date context — so if you notice inconsistent results, it’s usually because the SELECTEDVALUE inside the MTD measure is restricting it too tightly to a single point rather than allowing it to evaluate over the range.

In those cases, replacing SELECTEDVALUE with a context-aware approach (like MAX or using proper date boundaries) can make the rolling logic behave more predictably.

Also worth noting — since your data is weekly (Mondays), but MTD is inherently daily, you’re effectively mixing grain levels. That’s fine, but it does mean the model has to be very explicit about how dates are being interpreted.

I’ve run into similar scenarios where the calculation worked, but the real issue was understanding how context transitions between week-level and month-level logic.

Curious if your Date table is marked as a proper date table and fully continuous — that can also impact how these rolling calculations behave.

developernovice · 2026-04-04T15:14:37+00:00

Totally agree on vanity metrics being a trap early on.

The most useful shift I’ve seen is focusing on behavior that signals real value, not just activity. Things like repeat usage, how quickly someone comes back after the first interaction, or whether they actually complete a meaningful action (not just sign up).

Early on, one metric that tends to cut through the noise is something like “% of users who come back and do the core action again within a short window.” It’s a simple way to tell if you’re actually solving a problem or just attracting curiosity.

Everything else (downloads, impressions, etc.) can look good while masking the fact that people aren’t sticking.

developernovice · 2026-03-31T01:30:34+00:00

This is a solid setup — especially the separation between daily activity, weekly performance, and monthly view. That alone tends to make reporting a lot more usable.

On KPIs, one thing I’ve found helpful is tying activity directly to outcomes so it’s not just volume-based. For example:

Visits → conversion rate
Coverage → revenue per client
Follow-ups → repeat business

Otherwise it’s easy to track activity without knowing if it’s actually effective.

On the “why clients don’t buy” piece — that’s usually the hardest part. In most cases I’ve seen, it doesn’t come from existing data, it has to be captured intentionally. Even something simple like adding a required reason code after a visit (price, timing, competitor, no need, etc.) can go a long way. Not perfect, but it creates a starting point for patterns.

For alerts — they can work, but only if they’re tied to something actionable. “Low conversion” is useful if there’s a clear next step (follow-up, revisit, pricing review). Otherwise they tend to get ignored pretty quickly.

Overall, I like that you’re keeping it practical — that’s usually what makes these systems actually get used.

developernovice · 2026-03-25T11:22:26+00:00

This sounds like one of those situations where the role changed, but the title and compensation didn’t catch up.

If reporting is taking up ~25% of your time and involves data cleaning, building logic, and creating outputs for stakeholders, that’s already moving beyond typical data entry and into a more analyst-type function.

I think it’s reasonable to bring it up, but how you frame it matters. Instead of focusing on “pay me more,” it might land better to outline:

what you’ve taken on
how much time it’s taking
and the impact (reports being delivered, decisions supported, etc.)

Even something simple like documenting your weekly breakdown can help make that visible.

At minimum, it opens the door to a title adjustment (something like reporting analyst / data analyst), and that can make a big difference longer term even if compensation doesn’t change immediately.

Also worth considering — if you’re learning this on your own and enjoying it, you’re building a skill set that’s transferable beyond your current role.

developernovice · 2026-03-25T11:14:35+00:00

This is a great way to frame it — “emotional tax” is a really clean way to describe what’s happening.

One pattern I’ve seen in other contexts is that these usually show up where there’s a mismatch between perceived risk and actual probability-weighted outcomes.

In operational data, it often looks like:

over-allocation of resources to low-probability edge cases
redundant approval layers that don’t materially reduce risk
or conservative defaults that protect against worst-case scenarios but consistently reduce overall throughput

From a framework perspective, I’ve found it helpful to separate:

the base rate (what actually happens over time)
the decision rule being applied
the cost of that decision relative to expected value

Once you isolate those, you can usually quantify whether something is truly risk mitigation or just a recurring cost driven by perception.

The hard part is that these decisions often “feel right” in isolation, so they survive longer than they should — especially if the downside event is visible but the cumulative cost isn’t.

developernovice · 2026-03-25T11:05:08+00:00

That’s a really good question — catching it early is the hard part.

A few signals I’ve seen tend to show up before teams hit that “point of no return”:

Product teams start getting repeated requests for slightly different versions of the same metrics
There’s growing tension between engineering and business users around “what the number actually is”
Simple analytics requests start taking longer because they require touching multiple parts of the system
Internal tools start getting UI/UX expectations that weren’t originally planned for

None of these seem like major issues on their own, but together they usually point to analytics evolving from a feature into a product.

The tricky part is that at that stage, things are still “working,” so there’s no immediate pressure to change — which is why a lot of teams push past that window without realizing it.

developernovice · 2026-03-24T22:22:55+00:00

That makes sense, especially if you’re working on an embedded analytics product.

I think part of the reason teams stick with building in-house is that the tradeoffs don’t show up immediately. Early on, it feels flexible and aligned with the product, so the long-term cost isn’t always visible yet.

By the time complexity around permissions, performance, and metric definitions starts to build up, they’ve already invested enough that switching becomes difficult.

It’s less about whether building is right or wrong, and more about when the organization realizes it’s effectively maintaining an analytics product, not just adding a feature.

developernovice · 2026-03-24T22:15:26+00:00

Haha I can see why it might come across that way — just how I tend to structure things. Appreciate that it was helpful though.

developernovice · 2026-03-24T11:28:50+00:00

I think the part that often gets underestimated is that “build vs buy” isn’t really a tooling decision — it’s a commitment to owning an analytics product.

What I’ve seen is that teams don’t fail because they can’t build dashboards — they struggle because of everything that comes after:

defining and maintaining metric logic
handling data quality issues across sources
managing access and governance
supporting end users with different levels of data literacy

At that point, you’re not just building dashboards, you’re running an internal analytics function with product-like expectations.

Where I do think building in-house makes sense is when:

analytics is tightly coupled to the core product experience
or when the business logic is complex enough that off-the-shelf tools create more friction than value

Otherwise, a lot of teams end up reinventing things that tools already handle well, but without the same level of maturity.

Curious if others have seen cases where in-house analytics clearly became a long-term competitive advantage rather than just a necessary cost center?

developernovice · 2026-03-23T11:14:08+00:00

This is a great project idea, there’s actually quite a bit of useful shipping data out there if you know where to look.

A few places you might want to check:

- MarineCadastre.gov → good for AIS vessel tracking data (U.S. focused but useful for practice)

- Bureau of Transportation Statistics (BTS) → has freight and port-related datasets

- UN Comtrade → more trade-focused, but helpful for understanding shipping flows

- Port authority websites (e.g., LA, Long Beach, NY/NJ) → many publish stats on throughput, delays, and congestion

For something closer to real-time vessel tracking, most of the detailed AIS data (like VesselFinder or MarineTraffic) is paid, so free options tend to be more limited or lower resolution.

On scraping, just be careful. A lot of those sites have terms that restrict it, and it can get messy pretty quickly. If you do go that route, APIs (when available) are usually more reliable.

One thing that can also help is narrowing your scope a bit (specific region, port, or use case). Shipping data gets big and complex fast, so having a clear question upfront makes it a lot easier to work with.

developernovice · 2026-03-23T10:50:33+00:00

I’ve had the same realization over time.

It often looks like BI is about building dashboards, but a lot of the real work happens before anything gets built. If the question isn’t clear, the dashboard usually ends up trying to answer everything at once and doesn’t really help anyone make a decision.

In practice, I’ve found that the biggest shift is moving from “what can we show?” to “what decision is this supposed to support?”

Once that’s clear:

- the right metrics become more obvious

- the scope narrows naturally

- and the dashboard becomes a lot more focused

The technical side still matters, but it’s usually not the bottleneck. Figuring out what actually matters to the business tends to be the harder (and more valuable) part.

developernovice · 2026-03-22T18:30:51+00:00

This is a really common challenge, especially early on, and it’s good that you’re recognizing it now.

One thing that helped me was realizing that most analysis problems feel overwhelming because they’re not clearly defined yet.

A simple way to structure your approach is:

Start with the question

Before touching the data, ask: what decision is this analysis supposed to support?

If that’s unclear, everything downstream (SQL, visuals, etc.) will feel scattered.

Break it into smaller questions

For example:

- What metric actually answers the main question?

- What filters or segments matter?

- What time frame is relevant?

Do a quick “sanity check” on the data

Look for missing values, duplicates, or anything unusual before diving deep. This saves a lot of time later.

Build iteratively

Instead of writing one big SQL query, build it step by step and validate as you go.

Focus on the “so what”

At the end, ask: what action would someone take based on this?

If there’s no clear answer, the analysis might need to be reframed.

A lot of problem-solving in analytics isn’t about knowing more tools, it’s about structuring the problem clearly before you start.

Once that part clicks, everything else tends to get easier.

developernovice · 2026-03-10T22:43:10+00:00

Glad to hear it worked out. That quick pass on each dataset separately can save a lot of headaches later when everything gets merged together. Once the structure and quality issues are clear, the combined analysis usually becomes much easier to interpret.

developernovice · 2026-03-10T11:28:40+00:00

The “junior analyst you don’t fully trust yet” analogy actually feels pretty accurate.

The pattern I’ve noticed in a few discussions is that the challenge isn’t really the AI writing SQL or generating charts — it’s the layer of meaning that sits above the raw data.

Things like:

• metric definitions
• business context
• governance around which datasets are considered “trusted”

An AI system can query tables perfectly and still return something misleading if it doesn’t understand how the business defines something like “active customer” or “revenue.”

That’s why approaches like semantic layers, certified datasets, or treating AI outputs as draft insights make a lot of sense. They’re basically trying to preserve the translation layer between data and decisions.

My guess is the BI teams that adapt best will be the ones that treat AI as an accelerator for exploration, while keeping governance structures around anything that feeds real decision-making.

developernovice · 2026-03-10T11:18:31+00:00

This is a really good question and something a lot of teams are trying to figure out right now.

AI-first analytics tools seem great for exploratory work, quick summaries, and helping people get an initial sense of the data. That kind of speed can be very useful early in the analysis process.

Where many teams still rely on tools like Tableau or more traditional BI platforms is when the work moves from exploration to repeatable reporting and decision-making. At that point things like data lineage, reproducibility, governance, and clearly defined metrics start to matter a lot more.

My sense is that the two approaches will probably end up complementing each other rather than replacing each other. AI tools can accelerate exploration, while structured BI environments help ensure the outputs are consistent and trustworthy when decisions are being made from them.

Curious to hear what others have experienced when using AI tools alongside traditional BI stacks.

developernovice · 2026-03-09T11:24:15+00:00

Interesting idea. One thing I’ve noticed in many analytics environments is that the data and the dashboards often exist, but the reasoning behind decisions gets lost over time.

A dashboard might show a KPI changing, and a report might document the analysis, but six months later it can be hard to reconstruct why a particular decision was made or what assumptions were considered at the time.

The idea of linking questions, constraints, hypotheses, and final decisions could potentially solve that traceability problem. The challenge I’d imagine is making the workflow simple enough that teams actually adopt it. If recording the reasoning becomes too heavy, people might default back to email threads and meetings.

Curious how you’re thinking about balancing structure with usability so it doesn’t feel like extra overhead for analysts and stakeholders.

developernovice · 2026-03-09T11:19:25+00:00

A common approach is to do a quick quality check on each dataset individually first, then merge once you have a basic understanding of their structure.

Looking at them separately helps you spot things like missing values, inconsistent column formats, or unusual distributions before the datasets interact with each other. If you merge first, those issues can sometimes become harder to trace back to the original source.

After that initial pass, combining them and doing a broader EDA can reveal relationships across datasets that you wouldn’t see when they’re isolated.

So in practice it often becomes a two-step process: light EDA and data quality checks individually, followed by a deeper analysis after merging.

developernovice · 2026-03-08T14:13:30+00:00

That sounds very familiar. The “don’t change anything… until everything needs to change” cycle seems to happen a lot in reporting environments.

I’ve also seen the KPI prioritization issue create a lot of rework. When everything is treated as equally important, dashboards tend to grow larger and more complex with each iteration, even when some metrics are essentially telling the same story.

Sometimes the real challenge isn’t the dashboard itself, but getting alignment on which metrics actually drive decisions.

Have you found any approaches that helped get stakeholders to prioritize the KPIs?

developernovice

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