3 ms·

> RAG Retrieval Augmented Generation (apparently) It's really helpful to define acronyms. HN has a broad readership - I follow AI and I had never come across

> RAG Retrieval Augmented Generation (apparently) It's really helpful to define acronyms. HN has a broad readership - I follow AI and I had never come across this one.

red-iron-pine 3y ago
thank you. i'm doing a Master's at a legit engineering school, and literally just had an assignment to survey AI and its underlaying tech, and even then I hadn't heard this acronym.
- QuantumGood 3y ago
  For more detail, finetuning means adjusting parameters based on a smaller, specific dataset to tailor the LLM's responses, but the model's underlying knowledge is fixed at the point of its last training update. It adjusts how existing knowledge is used, but doesn't add new facts or information post-training. It's more about tweaking responses, biases, and style, rather than updating its factual database. RAG combines a language model like GPT with a real-time search component. This allows the model to pull in information from external sources during its response generation process. Now the ability to access and integrate the most recent information is gained, which the language model alone wouldn't have. But even with RAG, results can disappoint. RAG works best on smaller chunks of information, but the knowledge in the underlying model gets in the way of accuracy. On our knowledgebase, in an area where there is a lot of mis- and not-quite-accurate info on the internet, it regularly provides inaccurate information—is unusable. As @treprinium points out below, you also have to "calibrate similarity "thresholds" to know the probability distribution of relevant/correct/incorrect chunks for any given sentence and what the N should be to reach them. It's not going to be perfect but you can end up with 90% accuracy on average which tends to be better than many full-text search solutions"
- wenc 3y ago
  Check the sibling comment for a primer on LLM concepts. I think if you survey AI at a high level you won’t encounter the term. But if you survey or keep track with LLMs it’s an important concept that is widely known and discussed. It’s very important to know about because it’s one of the few practical techniques in LLMs.
nycdatasci 3y ago
I agree that it's great to define the acronym for the community. But with all respect, one of the primary AI debates over the last year has focused on how to push more (potentially non-public) information into foundation LLMs like GPT-4. RAG has been pretty central to that discussion. hn.algolia.com shows 248 results for "RAG" over all time and 89.9% of those are from the last year.
- andybak 3y ago
  I'd heard the phrase but not the acronym - there's a difference. Acronyms are only good for communicating phrases that you hear frequently. For infrequent terminology it's verging on puzzle-solving. Sometimes you can connect the dots and sometimes you can't.
  - wenc 3y ago
    In learning any new area of study, I’ve found that sociology to be super important. Rather than just reading the literature, I try to be immersed on the community and to overhear what people are talking about (r/localllama on Reddit is a good start). RAG is the coin of the realm in those communities and beyond. It’s not infrequently mentioned — in fact rather the opposite. I’ve found that learning an area solely by reading the literature etc is necessary but insufficient because you don’t get a sense of which topics are important. I’ve made this mistake several times in my career and ended up working on things that no one cared about.
    - andybak 3y ago
      I think we're talking at cross purposes. I don't want to learn more acronyms. I want people to use less of them. (or rather use them more selectively). I'm trying to improve people's writing rather than improve my reading comprehension.
      - deleted 3y ago
        [deleted]
wenc 3y ago
Definitely good to define acronyms. Also, here’s a good primer on LLM concepts which should help in following discussions. https://flyte.org/blog/getting-started-with-large-language-models-key-things-to-know https://flyte.org/blog/getting-started-with-large-language-m... It’s a bit meta but it’s also useful to read with the help of LLMs. For the past little while I’ve been reading in areas outside my expertise with the help of ChatGPT-4 defining and breaking things down for me as I read. My comprehension speed has gone up tremendously and I can follow complex material without getting too lost.

> RAG Retrieval Augmented Generation (apparently) It's really helpful to define acronyms. HN has a broad readership - I follow AI and I had never come across this one.

red-iron-pine 3y ago

thank you. i'm doing a Master's at a legit engineering school, and literally just had an assignment to survey AI and its underlaying tech, and even then I hadn't heard this acronym.

QuantumGood 3y ago
For more detail, finetuning means adjusting parameters based on a smaller, specific dataset to tailor the LLM's responses, but the model's underlying knowledge is fixed at the point of its last training update. It adjusts how existing knowledge is used, but doesn't add new facts or information post-training. It's more about tweaking responses, biases, and style, rather than updating its factual database. RAG combines a language model like GPT with a real-time search component. This allows the model to pull in information from external sources during its response generation process. Now the ability to access and integrate the most recent information is gained, which the language model alone wouldn't have. But even with RAG, results can disappoint. RAG works best on smaller chunks of information, but the knowledge in the underlying model gets in the way of accuracy. On our knowledgebase, in an area where there is a lot of mis- and not-quite-accurate info on the internet, it regularly provides inaccurate information—is unusable. As @treprinium points out below, you also have to "calibrate similarity "thresholds" to know the probability distribution of relevant/correct/incorrect chunks for any given sentence and what the N should be to reach them. It's not going to be perfect but you can end up with 90% accuracy on average which tends to be better than many full-text search solutions"
wenc 3y ago
Check the sibling comment for a primer on LLM concepts. I think if you survey AI at a high level you won’t encounter the term. But if you survey or keep track with LLMs it’s an important concept that is widely known and discussed. It’s very important to know about because it’s one of the few practical techniques in LLMs.

nycdatasci 3y ago

I agree that it's great to define the acronym for the community. But with all respect, one of the primary AI debates over the last year has focused on how to push more (potentially non-public) information into foundation LLMs like GPT-4. RAG has been pretty central to that discussion. hn.algolia.com shows 248 results for "RAG" over all time and 89.9% of those are from the last year.

andybak 3y ago
I'd heard the phrase but not the acronym - there's a difference. Acronyms are only good for communicating phrases that you hear frequently. For infrequent terminology it's verging on puzzle-solving. Sometimes you can connect the dots and sometimes you can't.
- wenc 3y ago
  In learning any new area of study, I’ve found that sociology to be super important. Rather than just reading the literature, I try to be immersed on the community and to overhear what people are talking about (r/localllama on Reddit is a good start). RAG is the coin of the realm in those communities and beyond. It’s not infrequently mentioned — in fact rather the opposite. I’ve found that learning an area solely by reading the literature etc is necessary but insufficient because you don’t get a sense of which topics are important. I’ve made this mistake several times in my career and ended up working on things that no one cared about.
  - andybak 3y ago
    I think we're talking at cross purposes. I don't want to learn more acronyms. I want people to use less of them. (or rather use them more selectively). I'm trying to improve people's writing rather than improve my reading comprehension.
    - deleted 3y ago
      [deleted]

wenc 3y ago

Definitely good to define acronyms. Also, here’s a good primer on LLM concepts which should help in following discussions. https://flyte.org/blog/getting-started-with-large-language-models-key-things-to-know https://flyte.org/blog/getting-started-with-large-language-m... It’s a bit meta but it’s also useful to read with the help of LLMs. For the past little while I’ve been reading in areas outside my expertise with the help of ChatGPT-4 defining and breaking things down for me as I read. My comprehension speed has gone up tremendously and I can follow complex material without getting too lost.