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Overview

This lesson teaches learners how to apply advanced prompt engineering techniques to practical tasks such as code generation, debugging, translation, and multimodal interactions. We also introduce automatic prompt optimization, a technique to let LLMs help refine their own prompts.

Concept Explanation

1. Automatic Prompt Engineering (APE)

• LLMs can self-optimize prompts through iterative improvement.
• Steps:
  1. Provide an initial prompt.
  2. Ask the LLM to analyze and improve it.
  3. Generate outputs using the improved prompt.
• Benefits:
  • Reduces manual trial-and-error.
  • Improves accuracy and creativity for repetitive tasks.

Example:

Original Prompt: "Explain recursion in Python."
Automatic Refinement: "Explain recursion in Python with a real-world analogy and a small code example."

2. Code Prompting

LLMs excel at code-related tasks. Techniques include:

a) Writing Code

• Generate new code from natural language descriptions.
• Example:

Prompt: "Write a Python function to reverse a linked list."

b) Explaining Code

• Ask the LLM to explain code line-by-line.
• Example:

Prompt: "Explain the following Python code: def factorial(n): ..."

c) Translating Code

• Convert code between languages.
• Example:

Prompt: "Convert this Python function to JavaScript."

d) Debugging & Reviewing Code

• Identify errors and suggest improvements.
• Example:

Prompt: "Find and fix bugs in this Python function."

3. Multimodal Prompting

• LLMs increasingly support text + images or other inputs.
• Examples:
  • Text description → image captioning.
  • Image + text → question answering.
• Use cases: AI assistants, design generation, documentation analysis.

4. Combining Techniques

• For complex tasks, combine:
  • Automatic prompt engineering.
  • Few-shot or chain-of-thought reasoning.
  • System/role instructions.
• Ensures outputs are accurate, structured, and aligned with user goals.

Practical Examples / Prompts

1. Automatic Prompt Engineering

Prompt: "Summarize this article." 
Refinement Request: "Improve this prompt for clarity, conciseness, and bullet-point output."

2. Code Writing

Prompt: "Write a Python function to sort a list of integers using merge sort."

3. Code Translation

Prompt: "Convert this Python function to Java: def add(a, b): return a + b"

4. Multimodal Prompt

Prompt: "Analyze this image and describe any objects you see." 
Input: [Image of a kitchen]

Hands-on Project / Exercise

Task: Build a mini AI assistant that can write, explain, and debug code.

Steps:

1. Choose a simple programming problem (e.g., calculator or data parser).
2. Write a base prompt for generating code.
3. Use few-shot examples for explanations or translations.
4. Apply automatic prompt refinement to improve outputs.
5. Test multiple completions and document which prompt yields the best results.

Goal: Produce an LLM workflow that reliably creates, explains, and debugs code.

Tools & Techniques

• APIs: OpenAI GPT, Vertex AI, Claude, LangChain for chaining prompts.
• Automatic Prompt Optimization: Let the model refine itself.
• Few-shot + CoT: Guide reasoning in code generation.
• Multimodal support: Explore text + image inputs for richer applications.

Audience Relevance

• Developers: Automate code writing, translation, and debugging.
• Students: Learn AI-assisted programming and reasoning.
• Business Users: Create internal tools for reporting, analysis, or documentation.

Summary & Key Takeaways

• Automatic prompt engineering reduces manual trial-and-error.
• LLMs are powerful for writing, explaining, translating, and debugging code.
• Multimodal inputs expand AI applications beyond text.
• Combining techniques (APE + CoT + system prompts) produces robust and structured outputs.
• Hands-on experimentation is key to mastering practical prompt engineering.