The Vertical AI Showdown: Prompt engineering vs Rag vs Fine-tuning

Summary

This video dives into the fierce competition over optimizing large language models (LLMs) for vertical AI applications, a market valued at over $100 billion. The speaker, Basil Fateen, explores three key methods: prompt engineering, retrieval augmented generation (RAG), and fine-tuning, detailing their strengths and weaknesses for domain-specific use cases. Using AWS services like Bedrock, Lambda, and S3, the video illustrates how to implement these techniques practically, with a focus on building vertical AI products. The speaker emphasizes the importance of domain expertise and quality data in crafting effective AI solutions.

Takeaways

• 😀 Vertical AI is a rapidly growing market, with over $100 billion at stake, and it's currently seeing intense rivalry over the best methods to optimize large language models (LLMs) for domain-specific use cases.
• 😀 The concept of vertical AI is rooted in vertical SaaS, where solutions are specialized for specific industries rather than generalized, allowing businesses to create deeper, more valuable products.
• 😀 To build a successful vertical AI application, you need two key components: domain expertise (deep knowledge of the industry) and high-quality, domain-specific data.
• 😀 There are three main optimization techniques for vertical AI: prompt engineering, retrieval augmented generation (RAG), and fine-tuning. Each has its strengths depending on the task and available resources.
• 😀 Prompt engineering enhances the input to the LLM to produce more relevant and high-quality outputs, which is ideal for quick adaptations but not for complex tasks or large datasets.
• 😀 Retrieval Augmented Generation (RAG) combines LLMs with a knowledge base, enabling the model to query external data sources for more accurate and up-to-date results, making it ideal for dynamic knowledge tasks.
• 😀 Fine-tuning involves training an LLM on a custom dataset specific to the domain, allowing the model to learn and adapt its weights, offering the highest level of customization and reasoning for specialized tasks.
• 😀 AWS tools like Amazon Bedrock, Lambda, and API Gateway are integral to deploying vertical AI applications, helping streamline communication with LLMs and making the development process more efficient.
• 😀 Amazon Q Developer is a valuable tool for transforming data into the proper format for fine-tuning, saving time and reducing the complexity of the data preparation process.
• 😀 Choosing the best optimization method depends on your specific use case: prompt engineering is suitable for quick adaptations with limited data, RAG works well for tasks requiring frequent updates, and fine-tuning is best for specialized tasks with substantial data.

Q & A

What is the main rivalry discussed in the video?

The video discusses the rivalry in the tech community over the best method to optimize large language models (LLMs) for domain-specific use cases, particularly in the vertical AI market.

What is the value of the vertical AI market mentioned in the script?

The vertical AI market is valued at over 100 billion dollars.

How does vertical AI relate to vertical SaaS?

Vertical AI is compared to vertical SaaS, where SaaS products offer specialized solutions for specific industries, similar to how vertical AI targets domain-specific use cases to optimize LLMs.

What are the two key components required to build a vertical AI product?

The two key components are domain expertise and data. Domain expertise involves understanding the task or problem, while data refers to the specific information required to enhance an LLM's capabilities.

What is the role of prompt engineering in vertical AI?

Prompt engineering involves enhancing the input prompt to improve the relevance and quality of the LLM's response, which is crucial for vertical AI use cases. It includes methods like few-shot prompting and system prompts to tailor the model to specific tasks.

What does Retrieval Augmented Generation (RAG) involve?

RAG involves creating a knowledge base with domain-specific documents that are indexed and embedded in a vector store. The LLM consults this knowledge base to enhance the response with relevant information.

How does fine-tuning differ from prompt engineering and RAG?

Fine-tuning involves using custom data to train an LLM, altering its weights to make it better suited for specific tasks. Unlike prompt engineering and RAG, which adjust inputs or augment data, fine-tuning directly modifies the model itself.

What is Amazon Bedrock and how is it used in vertical AI?

Amazon Bedrock is a hub for generative AI on AWS, where users can access models, create knowledge bases, and manage agents. It is used to orchestrate the processes of prompt engineering, RAG, and fine-tuning for vertical AI applications.

What is the significance of using a knowledge base in RAG?

The knowledge base in RAG contains specialized, domain-specific information that the LLM consults to enhance its responses. This allows the model to handle complex tasks with accurate, up-to-date information.

What is the recommended approach when you have complex tasks or a large amount of data?

When you have complex tasks or large amounts of data, fine-tuning is generally recommended. This method customizes the LLM to better understand the specific domain and task.

How does Amazon Q Developer assist in building vertical AI applications?

Amazon Q Developer helps by generating Python scripts for data transformation, ensuring that the data is formatted correctly for fine-tuning models. It saves time by automating parts of the data preparation process.