RAG vs. Fine Tuning

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Join Cedric Clyburn as he explores the differences and use cases of Retrieval Augmented Generation (RAG) and fine-tuning in enhancing large language models. This video covers the strengths, weaknesses, and common applications of both techniques, and provides insights on how to choose between them using machine learning and natural language processing principles
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#AI #LargeLanguageModels #FineTuning #RAG #ReinforcementLearning #MachineLearning #NaturalLanguageProcessing

[@Kk-ed1gr]

Thank you for the clarification, I had this question in mind last week, and I am glad that you have provided the answers I need.

[@IBMTechnology]

Glad it was helpful!

[@yusufersayyem7242]

35 minutes after downloading the clip, I received a notification, perhaps due to the weak internet in my country.... Finally, I would like to thank you sir for this wonderful explanation

[@IBMTechnology]

You're welcome!

[@FauziFayyad]

I have just watched the 1 years ago, then it updated today. Amazingg 🎉

[@educationrepublic9273]

Love IBM's short and sharp explainers! Thank you for an excellent video once again :)

[@Criszusep]

Euro 2024 World Championship. Nice... of course the LLM could't give a response 😂

[@umakrishnamarineni3520]

The RAG isn't updated with new tournament 😂😅

[@florentromanet5439]

I wanted to scream "WHY NOT BOTH⁉️) until 7:35 😂

[@MukeshKala]

Great explanation ❤

[@bharathYerukola-gt7vt]

Make a vedio on termonolgioes are often used on ai like benchmark and art of the state and etcc ❤

[@infotainmentunlimitedbyrohit]

Thank you 🙏💛

[@mark-lq4rk]

Thank you for the fascinating presentation. Assume certain conditions are similar, how would the cost of rag and fine-tuning differ?

[@IBMTechnology]

RAG is generally more cost-efficient than fine-tuning because it limits resource costs by leveraging existing data and eliminating the need for extensive training stages.

[@GG-uz8us]

I would like to see a real app that is in production with RAG and fine-tuning.

[@Siapanpeteellis]

What happens to a model when it is fine-tuned? do you use a database for RAG?

[@cloudnativecedric]

Good question! So with fine-tuning, using an approach like PEFT (Parameter-Efficient Fine-Tuning) which only updates a subset of the full model's parameters, we have new model weights and biases, which could then shared, deployed on a server, etc. for model inferencing with AI-enabled applications. For RAG, yes indeed, the most common method is with a vector database and turning your data into embeddings to search for similarity when using the LLM. But, there's other ways of setting up RAG pipelines too :)

[@jasonrhtx]

@@cloudnativecedricWhen would it make sense to first use PEFT, then apply RAG? Do both PEFT and RAG assign/label semantic relationships to the texts of user-added corpora and store these in a graph database?

[@JamilaJibril-e8h]

Uhhh okay i see you .....😂😂😂

[@memehub2002]

cool

[@einjim]

So, You are all told to wear your watch on your right hand right?!

[@bharathYerukola-gt7vt]

Nice vedio and also make a vedio on neural networks in deep like how neiral network is interlinked with deep learning and machine learning and what is actaully neuarl network and architecuture and why architectute is inporatnt fir neural networks and what is neural network actalkuy like a technique or mathematical expression or anything else so make a vedio on all these

[@hi5wifi-s567]

Using “Fine Tuning” , then machine ( accounting software) can be a bookkeeper to prepare financial records for …?

[@cloudnativecedric]

Just some ideas from the top of my head for fine-tuning with financial records are preparing financial statements, tax preparation (fine tuning on region-specific tax rules and historical data), expense tracking & categorization, etc.

[@ggggdyeye]

sir can you tell me how to make the vectorstore and store it in a specific file to use it every time.

[@SandraGarcia-t1k]

White Deborah Wilson Susan Garcia Cynthia

[@cho7official55]

I thought the retriever was on the far right, and llm in the middle of both, was I wrong, partially, is that schematic representation doesn't fathom all of the architecture, I'd like to go deeper on that matter.

[@cloudnativecedric]

There are a lot of variances with the RAG approach that can lead to different architectures, but there's a full video on the IBM Technology channel that dives into RAG as well!

[@SandraGarcia-t1k]

Garcia Kimberly Lopez Karen Hall Mark

[@harryli7557]

Large Manguage Model! 2:08

[@andiglazkov4915]

Thanks ☺️

[@IBMTechnology]

You're welcome!

[@ElaraArale]

Thank you~!

[@IBMTechnology]

You're welcome!

[@atanasmatev9600]

Large Language model is "LMM"?

[@cloudnativecedric]

Whoops! Good catch, sometimes I mess up when speaking and writing at the same time, it should be “LLM”.

[@rfflduck]

Great video!

[@IBMTechnology]

Thanks for the visit