Here is the birds eye view of Gardel s eye architecture.
It outlines the intricate process involving rails, prompts, outputs and guards.
Let's talk about the rails pack.
Picture this as the architect's blueprint for our guard rails system.
It outlines all the operational parameters, setting the stage for how the guardrails will function.
It's the groundwork that, when combined with a guard object, establishes our guard rails.
Speaking of the guard object, think of it as the diligent enforcer of the rails spec.
It acts like a vigilant supervisor, meticulously ensuring that all the standards we have set are consistently
applied.
So throughout our processes, it's the muscle behind the plan, ensuring everything runs as it should.
Now how do we move from theory to practice?
That's where the rail spec two prompts come into play.
This is the crucial step where our rail spec is combined or transformed onto a Onto a concrete prompt.
It's the transition point from our blueprint to actionable instructions for MLM.
And what about the prompt itself?
This is essentially the command center of the operation.
Here we feed instructions to MLM, guiding it on what to do.
The precision and relevance of AI's response heavily depends on the quality of these prompts.
It's where we direct the AI, ensuring it understands exactly what's expected.
Finally, we reach the guardrails.
This is where the real magic happens.
Our guardrails are in place to perform real time quality checks on the outputs generated by the MLM.
If an output fails to meet our standards, the guardrails step in to intervene.
They make the necessary adjustments to ensure everything aligns with our quality and ethical benchmarks.
Let's dive deeper into each of these components and see how they contribute to integrity and efficiency
of our AI systems.