Prompt Engineering for LLMs (book)
"Prompt Engineering for LLMs" by John Berryman and Albert Ziegler, provides a detailed guide to building applications with Large Language Models (LLMs). It begins with a history of LLMs and their core architecture, particularly the Transformer and its autoregressive nature, explaining concepts like tokens and logprobs. The text then shifts to practical techniques, discussing how to effectively craft prompts, manage context through methods like retrieval augmented generation (RAG) and summarization, and structure responses. Key aspects of LLM workflows, including the use of tools and the evolution of models through techniques like fine-tuning and Reinforcement Learning from Human Feedback (RLHF), are explored, culminating in strategies for evaluating LLM application quality through both offline and online methods.
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Visa alla i listan →Episode 10: The Judge – The Art and Science of Evaluating LLM Applications
The final, but perhaps most important step: evaluation. What are we actually testing? Dive into offline evaluation with example suites, how to find samples, evaluating solutions (including SOMA assessment), and online evaluation through A/B testing and various metrics. Learn to ensure quality and effectiveness in your LLM projects.
Episode 9: The Architect – Designing Basic and Advanced LLM Workflows
When is a conversational agent not enough? Discover basic and advanced LLM workflows. We look at how to define tasks, assemble workflows (with an example from Shopify plugin marketing), and explore concepts like LLM agents driving the workflow, stateful task agents, roles, and delegation.
Episode 8: The Conversation Master – Building Agents with Tools and Reasoning
Go beyond simple chats. Learn about LLMs trained for tool usage, guidelines for defining tools, and how to enable reasoning through techniques like Chain of Thought and ReAct. We also explore context for task-based interactions and how to build and manage conversational agents for a better user experience.
Episode 7: The Conductor – Guiding and Refining LLM-Generated Content
How do you ensure the LLM's output is what you intended? We look at the anatomy of an ideal "completion," including the preamble, recognizable start and end markers, and postscript. Explore logprobs, how to assess the quality of generated content, using LLMs for classification, critical points in the prompt, and model selection.
Episode 6: The Puzzle – Constructing the Perfect Prompt
Learn the anatomy of an ideal prompt. We discuss how to adapt the prompt depending on whether you're aiming for an advice conversation, an analytical report, or a structured document. Explore formatting snippets, "inertness," few-shot examples, elastic snippets, and the relationships between prompt elements like position and dependency.
Episode 5: Feeding the Beast – Crafting Effective Prompt Content
The content of your prompt is crucial. We explore different sources of content, from static to dynamic. Learn about the importance of clarifying your question, the power of "few-shot prompting," how to find dynamic context, the basics of Retrieval-Augmented Generation (RAG), and summarization techniques.
Episode 4: The Building Blocks – Designing and Evaluating LLM Applications
How do you actually build an application with an LLM at its core? We dissect "the loop" – from the user's problem to the model's output and back. Learn about the feedforward pass, the complexity of the loop, and how to evaluate the quality of LLM applications, both offline and online.
Episode 3: From Instruction to Interaction – The Evolution of Chat Models
Explore the transition from instruction-based LLMs to today's advanced chat models. We highlight the importance of Reinforcement Learning from Human Feedback (RLHF), its benefits, and the "alignment tax." Learn the differences between "instruct" and "chat models," how APIs have changed, and how prompt engineering can be likened to playwriting.
Episode 2: Behind the Curtain – How Language Models Think and See the World
Dive deep into the engine room of Large Language Models. We explore how LLMs process information, the differences compared to human thinking, and phenomena like "hallucinations." Learn about tokenization, auto-regressive models, the Transformer architecture, and how settings like "temperature" affect the output.
Episode 1: The Spark – Taming the Magic of Language Models
Join us on a journey back to the dawn of language models. We explore how early models paved the way for revolutionary technologies like GPT, and introduce the basics of prompt engineering – the art and science of communicating effectively with these powerful AI tools. Understand how we got here and why prompt engineering is the key to unlocking the full potential of LLMs.