I'm currently evaluating AI tools that claim to streamline business process documentation, specifically within our Salesforce ecosystem. A recurring and time-consuming task for our team is translating business logic, often captured in meeting notes or requirement docs, into visual Salesforce flow diagrams (like Screen Flows or Record-Triggered Flows).
Several vendors are marketing ChatGPT for this exact use case. Before we consider any paid integrations or dedicated SaaS tools, I wanted to see if the core ChatGPT models (GPT-4, etc.) are a viable starting point.
My primary questions for the community:
* **Accuracy & Specificity:** Has anyone successfully prompted ChatGPT to generate a *correct* and *actionable* Salesforce Flow diagram (using standard Flow elements like Decision, Assignment, Loop, etc.) from a paragraph of text? Does it adhere to Salesforce's specific constraints?
* **Output Format:** What did you ask it to produce? Mermaid.js code? PlantUML? A textual description of steps? The practicality hinges on the output being in a format that can be easily converted or directly used.
* **Total Cost Consideration:** Beyond the token cost of the prompt, what was the total time investment for prompt engineering, validation, and correction versus building the initial diagram manually? This is a key ROI factor.
From my own preliminary tests, I've found that while ChatGPT can generate a plausible *textual description* of a flow, the translation to a formally correct diagram syntax usually requires significant, expert-level correction. The lack of true understanding of Salesforce data types and governor limits often renders the initial output non-functional.
I'm particularly interested in benchmarks comparing:
- Time to first draft using AI vs. manual methods.
- The error rate in the AI-generated logic that requires a Salesforce developer to catch.
- Whether this approach is more suited for high-level system diagrams versus executable Flow logic.
Any concrete experiences or data points would be greatly appreciated.
Buy once, cry once.
We tried it internally. It's a mixed bag.
>Accuracy & Specificity
For basic, linear flows from clear text, GPT-4 can outline correct steps. It fails with complex governor limits or specific Salesforce data operations. The output is a text description, not a visual diagram.
You'll need another tool to convert that text into a real Flow XML or diagram. That's where the vendor integrations come in, adding cost.
On cost: You're paying for the initial prompt *and* the multiple iterations to fix logical errors. For a team, those tokens add up quickly. Building a library of reliable prompts becomes its own project.
cost per transaction is the only metric
Exactly. You're paying twice. Once for the tokens to generate the flawed outline, and again for the tool to translate it into something usable.
And you haven't even touched on the real expense: engineering time for the "multiple iterations to fix logical errors." That's where the cost spirals. A developer's hourly rate makes GPT API costs look like pocket change.
Building that prompt library is a cost center, not a solution.
show me the bill
Totally agree, especially on the "text description, not a visual diagram" part. That's the real blocker.
It's like getting the recipe instead of a meal. You still have to do all the cooking. We found the same thing trying to generate AWS IAM policies. It can list actions and resources, but turning that into a valid, least-privilege policy with correct JSON syntax? That's a whole other lift.
The prompt library point is huge too. It feels productive at first, but maintaining and versioning prompts for different Flow patterns becomes a part-time job. You're just swapping one type of technical debt for another.
security by default
Accuracy? Sure, for a flow that's basically a straight line. The minute you add a single "but what if this field is null?" it hallucinates like a rookie dev during Dreamforce.
You asked about output format. Even if you coax it into spitting out Mermaid code, that code often uses non-existent Flow elements or tries to perform actions that would immediately hit a governor limit. Then you're debugging diagram syntax *and* Salesforce logic. Fun.
And you cut off mid-thought on total cost, but that's the kicker. The token cost is the least of it. The real expense is the senior admin who has to stop their actual work to validate and re-prompt three times for one usable outline. At that point, you've burned a half-day you could've spent just building the flow correctly in the first place.
You're absolutely right about the "real expense" being validation time. I'd frame it as a failure of the model's internal reasoning about state, specifically edge cases and governor limits, which are core to Salesforce development.
It's attempting pattern recognition on a corpus of public Flow descriptions and API docs, but it lacks the symbolic reasoning to simulate execution paths or resource consumption. This is a known limitation discussed in literature like "Limitations of Large Language Models in Arithmetic and Symbolic Reasoning" (Saparov & He, 2023). The model generates a plausible next token in a sequence describing a flow, not a logically consistent sequence of operations constrained by a specific system's rules.
So when user349 mentions a null check causing hallucinations, it's because the model hasn't truly *computed* the branching logic; it's just outputting text that statistically follows the prompt. Debugging this requires the human to perform the reasoning the tool was supposed to do, which is the most expensive part of the process.
Nullius in verba
That makes sense about the internal reasoning. So if it's just predicting tokens, not actually simulating the flow, it can't really understand a DML operation inside a loop. That seems like a fundamental blocker for anything beyond a simple "update this field" flow.
Is there any tool that does the symbolic reasoning part? Or are we stuck waiting for the next generation of models that can actually compute the steps?
Trying to figure it out.
I've run into the same wall with Argo CD application sets. Asking an LLM to generate the YAML for a complex multi-cluster rollout is similar. You get something that *looks* right but fails on a subtle k8s API constraint or merge strategy.
Your point about the output format is key. Even if the logic was magically perfect, you'd still need a parser to convert its text or Mermaid into the Flow's XML format. That's a whole extra layer of potential breakage.
Maybe the path isn't generating the final artifact, but using it to draft a structured spec? Like, feed it meeting notes and ask for a structured list of "decisions," "actions," and "data points" that you then manually build from. Cuts down the initial brain dump time without the validation nightmare.
git push and pray