Complete AI Course (2025): Master Prompting, Evals, RAG & Fine-Tuning in 46 Minutes | Adam (Meta)

Name: Complete AI Course (2025): Master Prompting, Evals, RAG & Fine-Tuning in 46 Minutes | Adam (Meta)
Uploaded: 2025-06-01
Duration: 2782 s
Channel: Peter Yang
Description: Read a transcript preview of “Complete AI Course (2025): Master Prompting, Evals, RAG & Fine-Tuning in 46 Minutes | Adam (Meta)” from Peter Yang, uploaded on 2025-06-01.

Channel: Peter Yang

Uploaded: 2025-06-01

Duration: PT2782S

Read a transcript preview of “Complete AI Course (2025): Master Prompting, Evals, RAG & Fine-Tuning in 46 Minutes | Adam (Meta)” from Peter Yang, uploaded on 2025-06-01.

Transcript preview

Language: English (original, YouTube automatic captions)

[0:00] Basically, what we're talking about is

[0:01] like the different ways to improve and

[0:03] optimize AI's responses. How are they

[0:05] different from each other? Every prompt

[0:07] that I'm writing, it's sort of like you

[0:09] want it to just complete your thought at

[0:12] the end of it and and you sort of

[0:14] specify as much as you can. People

[0:15] shouldn't be surprised if they're not

[0:18] writing evals today. I think it's

[0:19] totally common like every business on

[0:22] Earth is just trying to figure out how

[0:23] to get AI into their product. What are

[0:26] some basic tips to make this thing

[0:28] respond? Well, I mean, it's useful to

[0:29] think of the system prompt separate from

[0:31] the main prompt. Why does Meta think

[0:33] open source is going to is going to be

[0:35] the right solution

[0:39] here. Okay, welcome everyone. My guest

[0:41] today is Adam Loving, AI partner

[0:44] engineer at Meta. Adam has helped

[0:46] hundreds of companies integrate AI into

[0:48] their products. So really excited to get

[0:50] him to do a technical deep dive on

[0:53] prompting eval retrieval argument

[0:56] generation fine-tuning and just building

[0:58] great AI products in general. This is

[1:00] going to be a complete course on AI from

[1:02] Adam. So so welcome Adam. Yeah, thank

[1:06] you. Thank you. Okay, so let's let's

[1:07] take a step back here and uh basically

[1:10] what we're talking about is like the

[1:11] different ways to improve and optimize

[1:13] AI's responses, right? And and like uh

[1:16] this is a great diagram. Maybe talk

[1:17] about like the two types the two

[1:19] different types of optimizations, you

[1:20] know, like how are they different from

[1:22] each other? Well, what we're optimizing

[1:24] for wh