Fundamentals of crafting effective prompts for LLMs, including instruction-following and goal alignment.

Using examples in prompts to improve performance on tasks like classification, reasoning, and translation.

Using descriptive task prompts without examples to elicit useful LLM responses with no fine-tuning.

Prompting technique that encourages step-by-step reasoning for complex tasks like math or logic.

Defining personas or roles within prompts to guide the style, tone, or perspective of LLM responses.

Strategies for providing relevant context and managing token limits when crafting effective prompts.

Designing prompts for conversational or context-aware systems where memory or message history matters.

Approaches to assess prompt effectiveness using metrics like accuracy, coherence, or LLM scoring.

Running comparative evaluations of prompt variants to choose the best-performing design.

Identifying causes of poor LLM output and iteratively refining prompt phrasing and structure.

Prompt strategies to mitigate hallucination, such as grounding with context, constraints, or retrieval.

Best practices for structuring prompts to generate or debug code using LLMs like GPT-4 or Claude.

Techniques for minimizing harmful, biased, or unsafe responses by crafting carefully constrained prompts.

Overview of tools like PromptLayer, LangSmith, and Guidance for prompt development and testing.

Using templating engines like Jinja2 or LangChain PromptTemplates to dynamically insert inputs into prompt formats.

Swift Lessons: Prompt Engineering

Tools