John Berryman is the author of the O'Reilly book "Prompt Engineering For LLMs" https://learning.oreilly.com/library/... Slides:

Slides, notes, links and more resources: https://parlancelabs.com/education/p...

00:00 Introduction and Background
John's career, from aerospace and search technology to GitHub Copilot.

00:47 Understanding Large Language Models
Definition and functionality of large language models. Importance of the "large" aspect. Historical progression: RNNs, attention mechanism, transformers. Emergence of models like BERT and GPT.

05:33 Overview of Prompt Crafting Techniques
Introduction to prompt crafting techniques. Focus on evolving techniques and recent trends.

06:09 FewShot Prompting
Technique: Controlling output with fewshot examples. Importance of setting predictable patterns.

07:39 Chain of Thought Reasoning
Addressing reasoning challenges in LLMs. Use of fewshot prompting to improve logical reasoning. CoT examples.

10:36 Think Step by Step
Simplification of chain of thought reasoning. Direct instruction to model for stepbystep thinking. Advantages: reduced need for extensive examples, prompt capacity management.

12:25 Document Mimicry
Technique of document mimicry in prompt crafting. Examples: transcripts, common document structures. Conditioning model with familiar patterns and formats like Markdown.

16:01 Intuitions for Effective Prompt Crafting
LLMs as "dumb mechanical humans." Use familiar language and constructs. Avoid overwhelming the model with too much information. Ensuring clarity in prompts.

18:11 Building Applications with LLMs
LLM applications as transformation layers. Converting user requests into LLMcompatible text. Process: user input, LLM processing, actionable outputs.

19:33 Context Collection for Prompt Crafting
Importance of context collection for prompt crafting. Steps: collecting, ranking, trimming, assembling context. Copilot example structure: file paths, snippets from open tabs, current document; document mimicry with comments. Importance of context relevance.

25:27 Introduction of Chat Interfaces
Shift to chatbased interfaces in LLM applications. Use of special syntax for role differentiation. Benefits of structured chat interactions.

28:22 Function Calling and Tool Usage With LLMs
Introduction and advantages of function calling. Structure: names, descriptions, arguments. Expansion of LLM capabilities with tool usage. Cycling through tool usage, tool responses, assistant responses.

33:56 Example: Tool Calling in a Thermostat Application
Detailed example: thermostat application. Process: user request, tool calling, context awareness. Iterative approach for better user interactions.

38:14 Q&A
Discussion on fewshot prompting best practices. Hyperparameter adjustments. Function calling complexities and solutions. Considerations for better code outputs and prompt tuning.