Are you a student or professional in , particularly involved with large language models (LLMs)? Understanding when and how to use various techniques like training from scratch, fine-tuning, prompt engineering, or Retrieval Augmented Generation (RAG) can be quite daunting. Each approach serves a unique purpose and comes with its cost-benefit analysis. AI Let’s demystify the process of optimizing LLM performance. Balancing factors like quality, cost, and ease of use to provide actionable insights. 🚀✨ I also want to share why Retrieval Augmented Generation (RAG) is becoming a game-changer in the field. Another very important distinction to understand is the difference between fine-tuning, basic prompting, and full-scale training from scratch and when to apply each of these methods for optimal results. Here’s a quick tl;dr: First, always start with prompt engineering with GPT-4 for quick and effective solutions. It doesn’t matter if it doesn’t scale. It will be fast and easy to set up and you can later on figure out how to go from there. When style-specific adaptations are required, try techniques on fine-tuning that are cost-effective methods like LoRa and QLoRa. Then, if imperfections like model hallucinations and misaligned outputs remain, retrieval augmented generation can significantly help at a relatively low cost. Now that you know the essentials, the next step is which one to take in your current position, considering YOUR challenges and how to do it. Here’s my new video that should answer all your questions: https://youtu.be/pHv9SsE4Mb4?embedable=true&transcript=true

How to Succeed on LinkedIn and Investing in Startups

Breaking Down Stable Video Diffusion: The Next Frontier in AI Imaging

Watch more on YouTube: https://www.youtube.com/c/WhatsAI

2021 - HackerNoon Contributor of the Year - DEEP-LEARNING

2021 - HackerNoon Contributor of the Year - FACEBOOK

Nominated for 2022 - Best Data Science Newsletter

Nominated for 2022 - HackerNoon Contributor of the Year - Artificial Intelligence

Nominated for 2022 - Top Tech Youtuber

Nominated for 2022 - HackerNoon Contributor of the Year - Innovation

Nominated for 2022 - HackerNoon Contributor of the Year - Data Science

Nominated for 2022 - HackerNoon Contributor of the Year - Natural Language Processing

Improving Your LLM: Train, fine-tune, prompt, RAG... What to do?!

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

From Novice to Data Pro in 90 Days: Avery Smith's Exclusive Method

AI as the "Bad Student" in Class

AI Will Not Kill Quantum Computing

AI's Unstoppable Energy Appetite: A Looming Crisis

Beyond the Algorithm: How Training Data Can Make or Break a Generative AI Model

From Novice to Data Pro in 90 Days: Avery Smith's Exclusive Method

AI as the "Bad Student" in Class

AI Will Not Kill Quantum Computing

AI's Unstoppable Energy Appetite: A Looming Crisis

Beyond the Algorithm: How Training Data Can Make or Break a Generative AI Model

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps