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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

Chapter 2: Advanced Prompting Strategies

Zero-Shot Prompting

Few-Shot Prompting

Instruction Following Prompts

Structuring Output Formats (JSON, Markdown)

Chain-of-Thought Prompting

Self-Consistency Prompting

Practice: Applying Advanced Techniques

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

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

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

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

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 Robust Output Handling

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

Prompt Design, Iteration, and Evaluation

© 2025 ApX Machine Learning

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