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Lessons12
One of the most important lessons I learned while working with AI-powered workflows is that an LLM does not need to make every decision.
An LLM is useful for interpreting natural language, but its output is probabilistic. Two similar messages can produce different wording or interpretations. This is acceptable for many conversational tasks, but it becomes risky when the syst
The critical separation is between interpreting language and making business decisions.
The LLM acts as an information extraction layer, not as the authority for workflow execution.
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
A multi-turn workflow needs a reliable memory of what has happened. That is different from merely sending the whole chat history back to an LLM.
A workflow can be represented as a graph:
A useful concept I learned is Out-of-Scope (OOS) Intent Detection, sometimes called Out-of-Domain (OOD) Intent Handling.
A robust architecture uses multiple layers of protection:
A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.
An important lesson is to test the deterministic engine without depending on live LLM variability.
Another useful architectural distinction is between a workflow engine and the application that uses it.
Interview questions12
One of the most important lessons I learned while working with AI-powered workflows is that an LLM does not need to make every decision.
An LLM is useful for interpreting natural language, but its output is probabilistic. Two similar messages can produce different wording or interpretations. This is acceptable for many conversational tasks, but it becomes risky when the syst
The critical separation is between interpreting language and making business decisions.
The LLM acts as an information extraction layer, not as the authority for workflow execution.
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
A multi-turn workflow needs a reliable memory of what has happened. That is different from merely sending the whole chat history back to an LLM.
A workflow can be represented as a graph:
A useful concept I learned is Out-of-Scope (OOS) Intent Detection, sometimes called Out-of-Domain (OOD) Intent Handling.
A robust architecture uses multiple layers of protection:
A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.
An important lesson is to test the deterministic engine without depending on live LLM variability.
Another useful architectural distinction is between a workflow engine and the application that uses it.
Modules4
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module