Prompt Engineering and LLM Application Development
Chapter 1: Foundations of Prompt Engineering
Introduction to Large Language Models
What is Prompt Engineering?
Components of a Prompt
Basic Prompting Techniques
Understanding LLM Temperature and Other Parameters
Hands-on practical: Simple Prompt Experiments
Quiz for Chapter 1
Chapter 2: Advanced Prompting Strategies
Zero-Shot Prompting
Few-Shot Prompting
Instruction Following Prompts
Role Prompting
Structuring Output Formats (JSON, Markdown)
Chain-of-Thought Prompting
Self-Consistency Prompting
Practice: Applying Advanced Techniques
Quiz for Chapter 2
Chapter 3: Prompt Design, Iteration, and Evaluation
Principles of Effective Prompt Design
Managing Prompt Length and Context Windows
Iterative Prompt Refinement
Evaluating Prompt Performance
Automated Prompt Testing Approaches
Version Control for Prompts
Hands-on practical: Prompt Optimization Challenge
Quiz for Chapter 3
Chapter 4: Interacting with LLM APIs
Overview of Common LLM APIs (OpenAI, Anthropic, etc.)
API Authentication and Security
Making API Requests with Python
Understanding API Request Parameters
Processing API Responses
Handling API Errors and Rate Limits
Streaming Responses
Hands-on practical: Build a Simple Q&A Bot
Quiz for Chapter 4
Chapter 5: Building Applications with LLM Frameworks
Introduction to LLM Frameworks (e.g., LangChain)
Core Components: Models, Prompts, Parsers
Understanding Chains
Managing Memory in LLM Applications
Introduction to Agents
Using Tools with Agents
Hands-on practical: Develop an Agentic Application
Quiz for Chapter 5
Chapter 6: Integrating LLMs with External Data (RAG)
Limitations of Standard LLM Knowledge
Introduction to Retrieval Augmented Generation (RAG)
Document Loading and Splitting
Text Embedding Models
Introduction to Vector Stores
Implementing Semantic Search/Retrieval
Combining Retrieved Context with Prompts
Basic RAG Pipeline Implementation
Hands-on practical: Build a RAG Q&A System for Documents
Quiz for Chapter 6
Chapter 7: Output Parsing, Validation, and Application Reliability
Challenges with LLM Output Consistency
Prompting for Structured Data (Revisited)
Using Output Parsers
Data Validation Techniques (e.g., Pydantic)
Handling Parsing Errors
Implementing Retry Mechanisms
Moderation and Content Filtering APIs
Practice: Implementing Strong Output Handling
Quiz for Chapter 7
Chapter 8: Application Development Considerations
Structuring LLM Application Code
Managing API Keys and Secrets
Cost Estimation and Monitoring
Basic Caching Strategies
Testing LLM Applications
Simple Deployment Options (Serverless, Containers)
Hands-on practical: Containerizing a Simple LLM App
Quiz for Chapter 8
Evaluating Prompt Performance
Was this section helpful?
- Holistic Evaluation of Language Models, Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda, 2023 Transactions on Machine Learning Research DOI: 10.48550/arXiv.2211.09110 - Offers a comprehensive framework for evaluating language models across various capabilities, scenarios, and metrics, providing a systematic approach to understanding model performance and limitations.
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, Nils Reimers and Iryna Gurevych, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (Association for Computational Linguistics) DOI: 10.18653/v1/D19-1410 - Presents a method for deriving semantically meaningful sentence embeddings that can be used for tasks like semantic similarity, directly supporting the automated metrics section.
- BLEU: a Method for Automatic Evaluation of Machine Translation, Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu, 2002 Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics) DOI: 10.3115/1073083.1073135 - A foundational paper introducing the BLEU metric, widely used for evaluating the quality of text generated by machine translation systems, applicable to other generative tasks.