TRACK 09
Foundations: RAG, prompting and fine-tuning
The core techniques for adapting LLMs, and how to choose between them.
5 articles- 01
From Search to Generation: Unlocking the Power of Retrieval-Augmented Generation (RAG)
Imagine you're chatting with an AI assistant. You ask it a complex question, and rather than giving you vague, generic advice, it offers a detailed, accurate response grounded in real-world facts. The secret behind such a feat?…
· 4 min read - 02
The Art and Science of Prompt Engineering: Unlocking LLM Potential
As Large Language Models (LLMs) continue to reshape our technological landscape, the role of prompt engineering has emerged as a crucial discipline at the intersection of human communication and artificial intelligence. In the past…
· 5 min read - 03
Fine-tuning LLMs: A Deep Dive into Instruction Fine-tuning
As we navigate the evolving landscape of language models, fine-tuning has emerged as a crucial technique for adapting these powerful tools to specific needs. Through my experience focusing on instruction fine-tuning, combined with…
· 4 min read - 04
Choosing the Right Approach: RAG, Prompt Engineering, or Fine-Tuning
In the landscape of generative AI and Large Language Models (LLMs), choosing the right technique for solving a particular problem can feel overwhelming. RAG, Prompt Engineering, and Fine-Tuning are three powerful tools in your AI…
· 3 min read - 05
From RAG to Hybrid Architectures
The landscape of enterprise information retrieval is rapidly evolving beyond basic RAG implementations. While the foundational RAG approach revolutionized how AI systems access and utilize information, today's enterprise needs are…
· 3 min read