🧠AI Concepts in a Nutshell: LLM Optimization

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.

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RAG combines an LLM's reasoning with external knowledge through three steps:
1️⃣ Retrieve: Identify related documents from an external knowledge base.
2️⃣ Augment: Enhance the input prompt with these documents.
3️⃣ 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!

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Fine-tuning adjusts the LLM's weights using proprietary data, extending its capabilities to specific tasks.

Approaches to Fine-Tuning:
1️⃣ Supervised Fine-Tuning: Uses demonstration data with input-output pairs.
2️⃣ 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.

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· RAG: Great for adding factual knowledge without altering the LLM. Easy to implement but adds extra components.
· 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.