Generative AI 101: When to use RAG vs Fine Tuning?

Leena AI
26 Feb 202406:07

Summary

TLDRIn this episode of 'JNA 101', the host discusses the optimal use of large language models (LLMs). They explain the cost and process of fine-tuning an LLM for specific data sets, highlighting its effectiveness in niche applications like predicting crop growth based on soil data. The host contrasts this with using LLMs out-of-the-box or through retrieval (RG), which is more cost-effective and integrates well with enterprise systems. The video concludes with advice on choosing between RG and fine-tuning based on the specific needs of the business.

Takeaways

  • 💡 Fine-tuning a large language model (LLM) is costly but necessary for specific use cases where you have unique data sets.
  • 📚 Fine-tuning requires a clean, bias-free dataset and investment in engineering and research efforts to adjust the model properly.
  • 🌱 An example of when fine-tuning is essential is in niche industries like agriculture, where predictive models for crop growth rely on proprietary data.
  • 🔍 Open-source LLMs may not have learned from niche data that is not readily available on the internet, making fine-tuning a better option for such specific cases.
  • 🚀 Fine-tuning is beneficial when you have access to exclusive data that can significantly enhance the LLM's performance in a particular application.
  • 🤖 Using an LLM directly is suitable for general purposes like chatting with virtual assistants but lacks integration with company databases.
  • 🔗 The real efficiency in enterprises comes from integrating LLMs with enterprise knowledge, such as documents and ERP systems.
  • 🔍 Retrieval (RG) is an alternative to fine-tuning that involves fetching relevant facts from enterprise systems for the LLM to generate responses.
  • 💰 RG is cost-effective as it doesn't require the computational resources and data preparation needed for fine-tuning.
  • ⚖️ The choice between RG and fine-tuning depends on the use case; RG is suitable for general inquiries, while fine-tuning is for specific business-related cases.
  • 📧 The speaker offers one-on-one discussions for further questions, encouraging viewers to reach out via social media channels.

Q & A

  • What is the main topic discussed in the video script?

    -The main topic is the decision-making process regarding when to use a fine-tuned large language model (LLM), when to use an out-of-the-box LLM, and when to use retrieval-augmented generation (RG).

  • Why is fine-tuning an LLM considered costly?

    -Fine-tuning is costly because it involves taking a pre-trained model, preparing a clean and bias-free dataset, and investing engineering and research resources to properly adjust the model for specific tasks.

  • What are the specific use cases where fine-tuning an LLM is recommended?

    -Fine-tuning is recommended when there is niche data available only to a few companies or not readily available on the internet, and when the LLM needs to perform optimally on tasks that are highly specific to that data.

  • Can you provide an example of a scenario where fine-tuning an LLM is beneficial?

    -An example is an agriculture company with decades of data on soil, nutrients, and bacteria that can be used to predict the best crop for a given land and season, which would greatly benefit from a fine-tuned LLM.

  • What is the primary limitation of using an out-of-the-box LLM for enterprise use?

    -The primary limitation is that an out-of-the-box LLM does not integrate with a company's database and lacks the ability to make sense of enterprise-specific knowledge without additional setup.

  • What does RG stand for, and how does it differ from fine-tuning an LLM?

    -RG stands for retrieval-augmented generation. It differs from fine-tuning by not requiring the LLM to be trained on new data but instead providing it with retrieved facts to generate responses.

  • Why is RG considered more efficient for large enterprises?

    -RG is efficient for large enterprises because it allows the LLM to access and make sense of enterprise knowledge from documents, ERP systems, CRM, etc., without the need for fine-tuning.

  • What percentage of use cases can be solved with RG according to the speaker's experience?

    -According to the speaker, 80% or more of use cases can be solved with RG.

  • How does RG help in providing accurate responses to enterprise queries?

    -RG helps by retrieving relevant facts from enterprise systems and providing them to the LLM, which then uses these facts to create accurate and contextually appropriate responses.

  • What is the speaker's offer to viewers who have questions on this topic?

    -The speaker offers to discuss one-on-one with viewers who have questions, encouraging them to reach out via social media channels for further discussion.

  • What is the main takeaway from the script regarding the use of LLMs in business?

    -The main takeaway is that businesses should choose between fine-tuning, using an out-of-the-box LLM, or RG based on the specificity of their data and use cases, with RG being a cost-effective option for general use cases and fine-tuning for specific business-related use cases.

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AI ModelsFine-TuningBusiness AIData IntegrationAgriculture MLEnterprise EfficiencyLLM APIRG RetrievalCost AnalysisCustom SolutionsAI Use Cases
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