Perplexity CEO explains RAG: Retrieval-Augmented Generation | Aravind Srinivas and Lex Fridman

Summary

The transcript delves into the intricacies of the Retrieval Augmented Generation (RAG) framework, emphasizing its role in generating responses by retrieving relevant documents and paragraphs to answer queries. It underscores the importance of sticking to factual information from the retrieved documents to ensure accuracy and avoid 'hallucinations.' The discussion also touches on the challenges of model comprehension, index quality, and snippet detail, which can lead to confusion or irrelevant answers. Furthermore, it explores indexing complexities, including web crawling, respecting robots.txt policies, and the decision-making process behind which URLs to crawl and how frequently. The summary concludes by highlighting the necessity of a hybrid approach combining traditional retrieval methods and modern embeddings for effective search.

Takeaways

• 😀 RAG (Retrieval-Augmented Generation) is a framework that retrieves relevant documents and uses them to generate answers to queries.
• 🔍 Perplexity, a more restrictive approach than RAG, ensures factual grounding by only allowing the model to use retrieved information to form responses.
• 📚 The model's ability to accurately retrieve and understand information is crucial for providing truthful and relevant answers.
• 🤖 Hallucinations in AI responses can occur due to model skill limitations, poor or outdated snippets, too much detail, or irrelevant documents.
• 🔗 The indexing process involves crawling the web, respecting robots.txt policies, and deciding on the frequency of crawling and the content to index.
• 🌐 Modern web pages require headless rendering to capture JavaScript-rendered content, which adds complexity to the indexing process.
• 📊 Indexing involves post-processing raw content into a format suitable for ranking, which can include machine learning and text extraction techniques.
• 📈 Traditional retrieval methods like BM25 are still effective and sometimes outperform newer embedding techniques in ranking documents.
• 📊 A hybrid approach combining traditional retrieval with modern embeddings and other ranking signals like domain authority and recency is necessary for effective search.
• 💡 The development of a high-quality search index requires significant domain knowledge and is a complex, time-consuming process.

Q & A

What is RAG and how does it work?

RAG stands for Retrieval-Augmented Generation. It is a framework that, given a query, retrieves relevant documents and selects pertinent paragraphs from those documents to generate an answer. It ensures that the generated answer is grounded in the retrieved text, enhancing factual accuracy.

What is the principle behind Perplexity's approach to AI?

Perplexity operates on the principle that the AI should not generate content beyond what is retrieved from documents. This ensures factual grounding and prevents the AI from producing nonsensical information or adding unverified details.

How does RAG ensure the accuracy of the information it retrieves?

RAG ensures accuracy by sticking to the information found in human-written text on the internet. It uses this text as a source of truth and cites it, making the AI's responses more controllable and reliable.

What are the potential issues that can lead to 'hallucinations' in AI responses?

Hallucinations in AI can occur due to several issues: 1) The model's inability to deeply understand the query or the retrieved text semantically, 2) Poor or outdated snippets in the index, and 3) Providing too much detail to the model, causing confusion and irrelevant information to be included in the response.

How can the quality of AI-generated answers be improved?

The quality can be improved by enhancing the retrieval process, improving the freshness and detail of the index, refining the model's ability to handle documents, and ensuring the model is skillful enough to recognize when it lacks sufficient information to provide a good answer.

What is the role of indexing in the RAG framework?

Indexing is a crucial part of the RAG framework. It involves building a searchable database of web content by crawling the internet, fetching and processing content, and converting it into a format suitable for retrieval and ranking.

How does the Perplexity bot decide what to crawl on the web?

The Perplexity bot decides what to crawl based on factors like the URLs and domains to prioritize, how frequently to crawl them, and respecting the robots.txt file which indicates the politeness policy set by website owners.

What challenges does the Perplexity bot face while crawling and indexing web pages?

The bot faces challenges like rendering modern websites that rely heavily on JavaScript, respecting the robots.txt file for politeness policies, and deciding the periodicity of recrawling to keep the index updated.

Why is it difficult to represent web page content using vector embeddings?

Vector embeddings face challenges because they need to capture the multifaceted nature of web content, including individual entities, specific events, and deeper meanings that might apply across different contexts.

What are some traditional retrieval methods that can complement vector embeddings in search?

Traditional retrieval methods like TF-IDF and BM25, which are based on term frequency and document relevance, can complement vector embeddings. These methods are still effective and can outperform pure embeddings in certain ranking tasks.

Why is a hybrid approach necessary for effective web search?

A hybrid approach is necessary because no single method, whether it's traditional term-based retrieval or modern embedding-based retrieval, can fully address the complexity of web search. Combining these methods allows for more accurate and relevant search results.

Outlines

00:00

🤖 Understanding Perplexity and RAG in AI

The paragraph introduces Perplexity, a framework for AI that adheres to the principle of not stating anything that hasn't been retrieved from documents. It contrasts with RAG (Retrieval Augmented Generation), which allows using additional context from retrieved documents to form answers. Perplexity ensures factual grounding by limiting the AI's responses to the exact information retrieved, aiming to avoid generating false or nonsensical information. The discussion touches on the challenges of model understanding at a semantic level and the potential for 'hallucinations' (erroneous information) due to model limitations, poor document quality, or excessive detail in the retrieved information.

05:01

🕷️ The Intricate Process of Web Crawling for Indexing

This section delves into the technical aspects of how web crawlers, such as the hypothetical 'Perplexity bot', operate. It discusses the decision-making processes involved in selecting which web pages to crawl, the frequency of crawling, and the importance of respecting robots.txt files to avoid overloading servers. The paragraph also highlights the complexity of modern web pages, which often require JavaScript rendering to understand the true content. The discussion includes the need for bots to respect crawl directives and the challenges of determining the periodicity of re-crawling and the incorporation of new pages based on hyperlinks.

10:02

📚 The Complexity of Indexing and Ranking in Search

The final paragraph explores the complexities of indexing and ranking in search engines. It discusses the need for a hybrid approach that combines traditional term-based retrieval methods like BM25 with more modern vector space representations. The paragraph emphasizes the importance of domain knowledge in search and the various signals that contribute to ranking, such as page rank, domain authority, and recency. It also touches on the limitations of pure vector embeddings for text and the ongoing debate around the effectiveness of different retrieval algorithms in the context of large-scale web data.

Keywords

💡Perplexity

Perplexity refers to a framework in natural language processing that aims to generate human-like text. In the context of the video, it's used to describe a system that retrieves relevant documents and uses them to construct answers to queries, ensuring factual grounding. The video discusses how Perplexity operates with a principle that the system should not generate content beyond what is retrieved, which helps maintain accuracy and reliability in the information provided.

💡RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a machine learning model framework that enhances text generation by retrieving relevant documents to assist in generating more informed responses. The video explains RAG as a method where given a query, the system retrieves relevant documents and paragraphs to inform the answer, thus enriching the context and improving the quality of the response.

💡Factual Grounding

Factual grounding is the practice of basing generated text on factual information retrieved from documents, ensuring that the generated content is accurate and reliable. The video emphasizes the importance of factual grounding in the context of Perplexity, where the system is designed to not generate information beyond what is retrieved from documents, thus preventing the creation of false or misleading information.

💡Hallucination

In the context of AI and natural language processing, 'hallucination' refers to the generation of incorrect or nonsensical information by a model due to a lack of understanding or insufficient data. The video discusses how 'hallucination' can occur in AI systems, particularly when the model fails to retrieve enough information or when the retrieved data is outdated, conflicting, or too detailed.

💡Indexing

Indexing in the context of search engines and AI systems refers to the process of organizing and storing data in a way that allows for efficient retrieval. The video delves into the complexities of indexing, such as crawling the web, respecting robots.txt policies, and deciding which URLs to prioritize. It also touches on the technical challenges of rendering web pages, extracting relevant content, and converting it into a searchable format.

💡Search

Search, as discussed in the video, involves the process of querying a database to retrieve relevant information. It is a critical component of AI systems like Perplexity, where the search function is used to find and retrieve documents that can be used to generate answers. The video highlights the importance of search in augmenting the generation process by providing additional context.

💡Snippets

Snippets are short, relevant pieces of text extracted from larger documents that are used to provide quick insights or answers to queries. The video discusses the importance of snippet quality in AI systems, noting that poor or outdated snippets can lead to 'hallucinations' where the system generates incorrect information.

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. The video mentions machine learning in the context of indexing, where algorithms are used to process and understand the content of web pages for efficient retrieval and ranking.

💡Ranking Algorithms

Ranking algorithms are used in search engines to order search results based on relevance to a query. The video explains that ranking is a critical part of the indexing process, where algorithms like BM25 are used to score and rank documents based on their relevance to a query, ensuring that the most pertinent information is surfaced.

💡Vector Space

Vector space is a mathematical concept used in natural language processing to represent text in a multidimensional space where each dimension represents a feature of the text. The video discusses the challenges of representing web page content in a vector space due to the complexity and variability of semantic meanings in text.

💡Domain Knowledge

Domain knowledge refers to the understanding and expertise in a specific area or subject. The video emphasizes the importance of domain knowledge in developing search systems, as it is crucial for understanding the nuances of different types of queries and for building effective indexing and ranking systems.

Highlights

RAG is a framework that retrieves relevant documents and uses them to generate answers.

Perplexity ensures factual grounding by only allowing information from retrieved documents.

RAG enhances answers by adding context from retrieved documents.

Hallucinations can occur if the model doesn't understand the query or retrieved documents deeply.

Poor or outdated snippets can lead to model confusion and incorrect information.

Overloading the model with too much detail can result in irrelevant information confusing the answer.

Skillful models can identify when retrieved documents are irrelevant and state the lack of information.

Improving retrieval, index quality, and snippet detail can reduce model hallucinations.

Indexing involves crawling the web, respecting robots.txt, and deciding which URLs to crawl.

Modern websites require headless rendering to accurately crawl and index content.

Respecting the crawl-delay set by websites is crucial to avoid overloading their servers.

Deciding the periodicity of recrawling and adding new pages to the queue is part of the indexing process.

Post-processing raw URL content is necessary for creating an index suitable for ranking systems.

Vector embeddings are not the only solution; traditional retrieval methods like BM25 are still effective.

BM25 can outperform embeddings in many retrieval benchmarks due to its sophistication.

A hybrid approach combining traditional and modern retrieval methods is necessary for effective search.

Ranking signals beyond semantic or word-based, such as page rank and recency, are important for search.

Search requires a significant amount of domain knowledge and is a complex problem to solve.

Transcripts

so can you speak to the technical

details of how perplexity Works you've

mentioned already rag retrieval

augmented generation what are the

different components here how does the

search happen first of all what is rag

yeah what does the llm do at at at a

high level how does the thing work yeah

so rag is retrieval augmented generation

simple

framework given a query always retrieve

relevant documents and pick relevant

paragraphs from each document and use

those documents and paragraphs to write

your answer for that query M the

principle and perplexity is you're not

supposed to say anything that you don't

retrieve which is even more powerful

than rag cuz rag just says okay use this

additional context and and and write an

answer but we say don't use anything

more than that too that way we ensure

factual grounding and if you don't have

enough information from documents you

retrieve just say we don't have enough

search results to can give you a good

answer yeah let's just Linger on that so

in general rag is doing the search part

with a query to add extra context yeah

to generate a uh a better answer I

suppose you're saying like you want to

really stick to the truth that is

represented by the human written text on

the internet and then cite it to that

text correct it's more controllable that

way yeah otherwise you can still end up

saying non ense or use the information

in the documents and add some stuff of

your own right despite this these things

still happen I'm not saying it's

foolproof so where is there room for

hallucination to seep in yeah there are

multiple ways it can happen one is you

have all the information you need for

the query the model is just not smart

enough to understand the query at a

deeply semantic level and the paragraphs

at a deeply semantic level and only pick

the relevant information and give you an

answer so that is a model skill issue

but that can be addressed as models get

better and they have been getting better

now the other place where hallucinations

can happen is you have uh poor

Snippets like your index is not good

enough ah yeah so you retrieve the right

documents or but but the information in

them was not up to date was stale or

are not detailed enough and then the

model had insufficient information or

conflicting information from multiple

sources and ended up like getting

confused and the third way it can happen

is you added too much detail to the

model like your index is so detailed

your Snippets are so you use the full

version of the

page and you threw all of it at the

model and ask it to arrive at the answer

and it's not able to discern clearly

what is needed and throws a lot of

irrelevant stuff to it and that

irrelevant stuff ended up confusing

it and made it like a bad answer so uh

all these three or the fourth way is

like you uh end up retrieving completely

irrelevant documents too but in such a

case if a model is skillful enough it

should just say I don't have enough

information so there are like multiple

Dimensions where you can improve a

product like this to reduce

hallucinations where you can improve the

retrieval you can improve the quality of

the index the fresh of the pages in the

index and you can include the level of

detail in the Snippets you can include

the uh improve the models uh ability to

handle all these documents really well

and uh if you do all these things well

you can keep making the product better

so it's kind of incredible I get to see

sort of directly because I've seen

answers uh in fact for for perplexity

page that youve posted about I've seen

ones that reference a transcript of this

podcast M and it's cool how it like gets

to the right snippet mhm like probably

some of the words I'm saying now and

you're saying now will end up in a

perplexing answer

possible it's crazy yeah it's very

meta including the Lex being uh smart

and handsome part that's out of your

mouth in a transcript forever now but if

the model is smart enough it'll know

that I set it as an example to say what

not to say

well not to say it's just a way to mess

with the model the model smart enough

it'll know that I specifically said

these are ways a model can go wrong and

it'll use that and say well the model

doesn't know that there's video

editing so the indexing is fascinating

so is there something you could say

about the some interesting aspects of

how the indexing is done yeah so

indexing is um you know multiple Parts

obviously you have to first build a um

cross

it's like you know Google has Google bot

we have flexity bot bang bot GPT bot

there's like a bunch of bots that crawl

the web how does perplexity bot work

like uh so that that's a that's a

beautiful little creature so it's

crawling the web like what are the

decisions it's making is it's crawling

the web Lots like even deciding like

what to put in the queue which we Pages

which domains and uh uh how frequently

all the domains need to get crawled and

um it's not just about like you know

knowing which

URLs this is like you know deciding what

URLs to crawl but um how you crawl them

you basically have to render headless

render and then websites are more modern

these days it's not just the

HTML um there's a lot of JavaScript

rendering uh you have to decide like

what's what's the real thing you want

from a page and obviously uh people have

robots the text file um and that's like

a politeness policy where you you should

respect the delay time time mhm so that

you don't like overload their servers by

continually crawling them and then

there's like stuff that they say is not

supposed to be crawled and stuff that

they allow to be crawl and you have to

respect that and uh the bot needs to be

aware of all these things and

appropriately craw stuff but most most

of the details of how a page works

especially with JavaScript is not

provided to the bot like has to figure

all that out yeah it depends if some

some Publishers allow that so that you

know they think it'll benefit their

ranking more mhm some Publishers don't

allow that and uh um you need to

like keep track of all these things per

domains and subdomains and it's crazy

and then you also need to decide the

periodicity yeah with which you

recrawl and you also need to decide what

new pages to add to this queue based on

like

hyperlinks so that's the crawling and

then there's a part of like building

fetching the content from each URL and

like once you did that through the

Headless render you have to actually

build the index now uh and you have to

reprocess you have to post-process all

the content you fetched which is the raw

dump into something that's inestable for

a ranking system so that requires some

machine learning text extraction Google

has this whole system called now boost

that extracts relevant metadata and like

relevant content from each uh raw URL

content is that a fuling machine

learning system is like embedding into

some kind of vector space it's not

purely Vector space it's not like once

the content is fetched there is some uh

Bird model that runs on all of it and uh

puts it into a big gigantic Vector

database which you R from it's not like

that uh because packing all the

knowledge about a web page into one

vector space representation is very very

difficult there's like first of all

Vector embeddings are not magically

working for text

it's very hard to like understand what's

a relevant document to a particular

query should it be about the individual

in the query or should it be about the

specific event in the query or should it

be at a deeper level about the meaning

of that query such that the same meaning

applying to different individual should

also be retrieved you can keep arguing

right like what should an representation

really capture and it's very hard to

make these Vector embeddings have

different dimensions be disentangled

from each other and capturing different

sem

so uh what retrieval typically this is

the ranking part by the way there's

indexing part assuming you have like a

post process version per URL and then

there's a ranking part that uh depending

on the query you ask FES the relevant

documents from the

index and some kind of score and that's

where like when you have like billions

of pages in your index and you only want

the top K you have to rely on

approximate algorithms to get you the

top okay so that's that's the ranking

but you also I mean that step of

converting a page into something that

could be stored in a vector

database it just seems really difficult

it doesn't always have to be stored

entirely in Vector databases there are

other data structures you can use sure

uh and other forms of uh traditional

retrieval that you can use uh there is

an algorithm called bm25 precisely for

this which is a more sophisticated

version of uh tfidf tfidf is term

frequency times inverse document

frequency a very uh uh old school

information retrieval system that just

works actually really well even today uh

and uh bm25 is a more uh sophisticated

version of that is still you know

beating most embeding on ranking wow

like when open AI released their

embeddings there was some controversy

around it because it wasn't even beating

bm2 on many many retrievable benchmarks

not because they didn't do a good job

bm25 is so good so this is why like just

pure embeddings and Vector spaces are

not going to solve the search problem

you need the

traditional uh termbase retrieval you

need some kind of engram based retrieval

so for the for the

unrestricted web data you can't just uh

you need a combination of all a hybrid

and you also need other ranking signals

outside of the semantic or word-based

this is like page ranks like signals

that score domain Authority and uh

recency right so you have to put some

extra positive weight on the recency but

not so it overwhelms and this really

depends on the query category and that's

why search is a hard lot of domain

knowledge invol problem yeah that's why

we chose to work on like everybody talks

about rappers competition models there

insane amount of domain knowledge you

need to work on this and it takes a lot

of time to build up towards like uh

highly really good

index with like really good ranking all

these signals