WEBVTT

00:00.840 --> 00:04.680
Here is the birds eye view of Gardel s eye architecture.

00:05.920 --> 00:11.360
It outlines the intricate process involving rails, prompts, outputs and guards.

00:14.200 --> 00:16.160
Let's talk about the rails pack.

00:17.200 --> 00:21.840
Picture this as the architect's blueprint for our guard rails system.

00:23.320 --> 00:29.680
It outlines all the operational parameters, setting the stage for how the guardrails will function.

00:30.440 --> 00:36.440
It's the groundwork that, when combined with a guard object, establishes our guard rails.

00:38.680 --> 00:43.600
Speaking of the guard object, think of it as the diligent enforcer of the rails spec.

00:45.160 --> 00:53.320
It acts like a vigilant supervisor, meticulously ensuring that all the standards we have set are consistently

00:53.320 --> 00:54.000
applied.

00:54.520 --> 01:01.480
So throughout our processes, it's the muscle behind the plan, ensuring everything runs as it should.

01:03.560 --> 01:06.280
Now how do we move from theory to practice?

01:07.160 --> 01:10.040
That's where the rail spec two prompts come into play.

01:10.880 --> 01:17.430
This is the crucial step where our rail spec is combined or transformed onto a Onto a concrete prompt.

01:18.390 --> 01:23.550
It's the transition point from our blueprint to actionable instructions for MLM.

01:25.870 --> 01:27.750
And what about the prompt itself?

01:29.550 --> 01:33.190
This is essentially the command center of the operation.

01:34.390 --> 01:38.150
Here we feed instructions to MLM, guiding it on what to do.

01:39.470 --> 01:45.470
The precision and relevance of AI's response heavily depends on the quality of these prompts.

01:46.790 --> 01:54.910
It's where we direct the AI, ensuring it understands exactly what's expected.

01:54.950 --> 01:57.270
Finally, we reach the guardrails.

01:57.910 --> 01:59.910
This is where the real magic happens.

02:00.470 --> 02:08.590
Our guardrails are in place to perform real time quality checks on the outputs generated by the MLM.

02:08.630 --> 02:13.710
If an output fails to meet our standards, the guardrails step in to intervene.

02:14.670 --> 02:21.470
They make the necessary adjustments to ensure everything aligns with our quality and ethical benchmarks.

02:22.830 --> 02:29.310
Let's dive deeper into each of these components and see how they contribute to integrity and efficiency

02:29.310 --> 02:32.910
of our AI systems.