RAG vs. Fine-Tuning
Choosing the Right Method for External Knowledge
In AI development, incorporating proprietary data and external knowledge is crucial. Two key methodologies are Retrieval Augmented Generation (RAG) and fine-tuning. Here’s a quick comparison.
๐๐๐ญ๐ซ๐ข๐๐ฏ๐๐ฅ ๐๐ฎ๐ ๐ฆ๐๐ง๐ญ๐๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง (๐๐๐) ๐
RAG combines an LLM’s reasoning with external knowledge through three steps:
1๏ธโฃ Retrieve: Identify related documents from an external knowledge base.
2๏ธโฃ Augment: Enhance the input prompt with these documents.
3๏ธโฃ Generate: Produce the final output using the augmented prompt.
The retrieve step is pivotal, especially when dealing with large knowledge bases. Vector databases are often used to manage and search these extensive datasets efficiently.
Implementing a RAG-Chain with Vector Databases: time to recall the post “AI concept in a Nutshell: LLM series – Embeddings & Vectors ” from 1 month ago!
๐ ๐ข๐ง๐-๐๐ฎ๐ง๐ข๐ง๐ ๐ ๏ธ
Fine-tuning adjusts the LLM’s weights using proprietary data, extending its capabilities to specific tasks.
Approaches to Fine-Tuning:
1๏ธโฃ Supervised Fine-Tuning: Uses demonstration data with input-output pairs.
2๏ธโฃ Reinforcement Learning from Human Feedback: Requires human-labeled data and optimizes the model based on quality scores.
Both approaches need careful decision-making and can be complex.
๐๐ก๐๐ง ๐ญ๐จ ๐๐ฌ๐ ๐๐๐ ๐จ๐ซ ๐ ๐ข๐ง๐-๐๐ฎ๐ง๐ข๐ง๐ ? ๐ค
ยท RAG: Great for adding factual knowledge without altering the LLM. Easy to implement but adds extra components.
ยท Fine-Tuning: Best for specializing in new domains. Offers full customizability but requires labeled data and expertise. May cause catastrophic forgetting.
Choose based on your needs and resources. Both methods have their strengths and challenges, making them valuable tools in AI development.
LLM Temperature
I love the analogy shared during one of the break-out sessions at the OVHcloud summit: LLM temperature is like blood alcohol level – the higher it is, the more unexpected the answers! ๐
To be more specific, temperature is a parameter that controls the randomness of the model’s output. A higher temperature encourages more diverse and creative responses, while a lower temperature makes the output more deterministic and predictable.
๐ Key Points:
๐น High Temperature: More random and creative outputs, useful for brainstorming and generating novel ideas.
๐น Low Temperature: More predictable and coherent outputs, ideal for factual information and structured content.
Understanding and adjusting the temperature can help tailor LLM outputs to specific needs, whether you’re looking for creativity or precision.
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Fostering and enhancing impactful collaborations with a diverse array of AI partners, spanning Independent Software Vendors (ISVs), Startups, Managed Service Providers (MSPs), Global System Integrators (GSIs), subject matter experts, and more, to deliver added value to our joint customers.
๐ CAREER JOURNEY โ Got a Master degree in IT in 2007 and started career as an IT Consultant
In 2010, had the opportunity to develop business in the early days of the new Cloud Computing era.
In 2016, started to witness the POWER of Partnerships & Alliances to fuel business growth, especially as we became the main go-to MSP partner for healthcare French projects from our hyperscaler.
Decided to double down on this approach through various partner-centric positions: as a managed service provider or as a cloud provider, for Channels or for Tech alliances.
โก๏ธ Now happy to lead an ECOSYSTEM of AI players each bringing high value to our joint customers.