Quiz: Retrieval-augmented generation (RAG), Clearly Explained (Why it Matters)

Test your understanding of the specific concepts, solutions, and technical details covered in this video about fixing AI hallucination problems through RAG and fine-tuning.

Technology mixed 8 Questions Video Quiz

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Q1 Beginner 1 pts

According to the video, what are the two main solutions presented for fixing AI context problems? Video Reference:
"We've actually got two solid ways to tackle this... The first option that we have is fine-tuning... ..."

Select all correct answers

Fine-tuning and RAG Embedding and Chunking Vector storage and GPU training Data intake and Processing

Q2 Intermediate 2 pts

What specific drawback does the video mention about fine-tuning AI models? Video Reference:
"GPU time is going to cost you an arm and a leg..."

GPU time is extremely expensive Models become too specialized Training takes too long Data becomes corrupted

Q3 Advanced 3 pts

What are the four steps of the RAG pipeline as described in the video? Video Reference:
"step number one is your data intake... step two, which is chunking... step three which is embedding...."

Select all correct answers

Data intake, Chunking, Embedding, Vector storage Training, Testing, Deployment, Monitoring Collection, Processing, Storage, Retrieval Indexing, Sorting, Storing, Retrieving

Q4 Intermediate 2 pts

According to the video, which specific tools can be used for the chunking process? Video Reference:
"So the tools that you can use for this is lang text split or you can also use llama index..."

Lang text split and llama index OpenAI and Google APIs Pine Corn and Chroma Vector databases only

Q5 Intermediate 2 pts

Which specific embedding models does the video mention as options? Video Reference:
"Popular models that you can use for this is Google's text embedding API or you can also use OpenAI's..."

Google's text embedding API and OpenAI's text embedding 3 Mistral and Grock Pine Corn and Chroma Claude and Gemini

Q6 Intermediate 2 pts

According to the video, which vector storage solutions are mentioned? Video Reference:
"So, you've got names like Pine Corn, Chroma, Qentrint in the Ring..."

Select all correct answers

Pine Corn, Chroma, and Qentrint MongoDB and MySQL Redis and Elasticsearch Vector and Matrix DB

Q7 Beginner 1 pts

The video compares RAG to which specific analogy? Video Reference:
"Let's imagine that you're a world-class chef. You know how to cook anything, but you don't know what..."

A world-class chef receiving recipes A librarian organizing books A teacher giving lessons A computer processing data

Q8 Intermediate 2 pts

What are the three specific advantages of RAG mentioned in the video? Video Reference:
"Number one is fast iterations... Next is cheap infrastructure... Next is it's always fresh..."

Select all correct answers

Fast iterations, cheap infrastructure, always fresh Speed, accuracy, reliability Cost, performance, scalability Efficiency, power, flexibility

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Quiz: Retrieval-augmented generation (RAG), Clearly Explained (Why it Matters)

According to the video, what are the two main solutions presented for fixing AI context problems? Video Reference: "We've actually got two solid ways to tackle this... The first option that we have is fine-tuning... ..."

What specific drawback does the video mention about fine-tuning AI models? Video Reference: "GPU time is going to cost you an arm and a leg..."

What are the four steps of the RAG pipeline as described in the video? Video Reference: "step number one is your data intake... step two, which is chunking... step three which is embedding...."

According to the video, which specific tools can be used for the chunking process? Video Reference: "So the tools that you can use for this is lang text split or you can also use llama index..."

Which specific embedding models does the video mention as options? Video Reference: "Popular models that you can use for this is Google's text embedding API or you can also use OpenAI's..."

According to the video, which vector storage solutions are mentioned? Video Reference: "So, you've got names like Pine Corn, Chroma, Qentrint in the Ring..."

The video compares RAG to which specific analogy? Video Reference: "Let's imagine that you're a world-class chef. You know how to cook anything, but you don't know what..."

What are the three specific advantages of RAG mentioned in the video? Video Reference: "Number one is fast iterations... Next is cheap infrastructure... Next is it's always fresh..."

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Calculating Your Results...

Source Video

According to the video, what are the two main solutions presented for fixing AI context problems? Video Reference:
"We've actually got two solid ways to tackle this... The first option that we have is fine-tuning... ..."

What specific drawback does the video mention about fine-tuning AI models? Video Reference:
"GPU time is going to cost you an arm and a leg..."

What are the four steps of the RAG pipeline as described in the video? Video Reference:
"step number one is your data intake... step two, which is chunking... step three which is embedding...."

According to the video, which specific tools can be used for the chunking process? Video Reference:
"So the tools that you can use for this is lang text split or you can also use llama index..."

Which specific embedding models does the video mention as options? Video Reference:
"Popular models that you can use for this is Google's text embedding API or you can also use OpenAI's..."

According to the video, which vector storage solutions are mentioned? Video Reference:
"So, you've got names like Pine Corn, Chroma, Qentrint in the Ring..."

The video compares RAG to which specific analogy? Video Reference:
"Let's imagine that you're a world-class chef. You know how to cook anything, but you don't know what..."

What are the three specific advantages of RAG mentioned in the video? Video Reference:
"Number one is fast iterations... Next is cheap infrastructure... Next is it's always fresh..."