An Open Course on LLMs, Led by Practitioners

2024-07-29 · Source: Hamel Husain's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, short

Summary

Parlance Labs has released "Mastering LLMs," a free, open survey course comprising workshops and talks from over 25 industry practitioners. Published on July 29, 2024, the course covers applied topics crucial for building AI products, including evaluations, Retrieval-Augmented Generation (RAG), fine-tuning, and prompt engineering. It is designed for technical individual contributors, such as engineers and data scientists, who possess basic LLM experience and seek guidance on enhancing AI products. The curriculum, totaling over 40 hours, is organized by subject area and includes chapter summaries, notes, slides, and additional resources to facilitate navigation and deeper learning. Notable speakers include Jeremy Howard, Sophia Yang, and Simon Willison.

Key takeaway

For technical individual contributors, including engineers and data scientists, seeking to improve AI products with LLMs, you should explore the free "Mastering LLMs" course. Focus on the applied topics like RAG and fine-tuning, and use the provided notes and resources to deepen your understanding. Applying the concepts to a personal project will solidify your learning.

Key insights

Industry veterans offer a free, applied LLM survey course covering evals, RAG, and fine-tuning for technical professionals.

Principles

• Prior art in ML applies to LLMs.
• Applied topics are key for AI product builders.

Method

The course material is organized by subject area, providing chapter summaries, notes, slides, and resources to help learners navigate over 40 hours of content and focus on relevant topics.

In practice

• Apply course learnings to a personal project.
• Review chapter summaries to quickly peruse topics.

Topics

• Large Language Models
• Retrieval-Augmented Generation
• Fine-tuning
• MLOps
• Prompt Engineering

Best for: Machine Learning Engineer, Data Scientist, MLOps Engineer

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• Your LLM Isn’t Dumb — It Just Lacks Your Context — LLMs need enterprise context; HITL feedback RAG injects human-curated corrections to improve accuracy without retraining.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hamel Husain's Blog.