A freely available set of workshops and talks on the essentials of LLMs, taught by practitioners. The topics include Evals, Retrieval-augmented-generation (RAG), Fine-tuning etc.

Most importantly it's hands down practical; it won't bore you with maths and the like, since it's focused on applied topics that are relevant to people building AI products.

With that said, the course's categories are:

• Evals
• RAG
• Fine-Tuning
• Applications
• Prompt Engineering

It is important to note, that this is not a course in the usual sense; there's no chapter 1, 2 and so on or a structured approach. On the contrary, it is composed of videos (and code) from different practitioners.

For instance, in the Eval category there's three sessions:

• Inspect, An OSS framework for LLM evals
• LLM Eval For Text2SQL
• A Deep Dive on LLM Evaluation

Three different sessions by three different presenters related just by the concept of LLM evaluation.

Next comes RAG or Retrieval Augmented Generation with:

• Beyond the Basics of RAG
• Back to Basics for RAG
• Systematically improving RAG applications

Fine-Tuning is the largest section with 16 available sessions addressing the question "Should you fine-tune?" and following up with tools and techniques for fine-tuning and  moving on to Advanced topics in fine-tuning. Then comes a section, Building Applications which currently has three sessions with a fourth "Python is All You Need" about building full-stack applications listed as being private and to be released shortly.

Prompt Engineering the smallest section with just one session about the key strategies employed for effective prompt engineering:

(1) Understanding prompt structure
(2) Best practices for crafting prompts
(3) Techniques for iterative improvement
(4) Domain-specific examples
(5) Avoiding common pitfalls

All the talks come with their associated transcript, slides and Python code.

To conclude, Mastering LLMs is totally focused on the practice of applying AI to create useful applications, as such it targets the new breed of the "AI Engineers", developers who unlike researchers are concerned with practically applying AI (models, tools, and APIs) on building software.

More Information

Mastering LLMs

Related Articles

Microsoft's Generative AI for Beginners Version 2

Free Course On ChatGPT Prompt Engineering

Take Vanderbilt's Prompt Engineering for ChatGPT For Free

Explore The Technologies Leading To ChatGPT, GPT4 & LLMs

To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin.

Comments

Make a Comment or View Existing Comments Using Disqus

or email your comment to: comments@i-programmer.info