Advanced Prompt Engineering / Advanced Prompt Structures

Pseudo-Code Prompting

Intermediate [3/5]
Code-style instructions Algorithmic prompting Structured logic prompting

Definition

Pseudo-code prompting uses programming-style syntax with control flow structures to provide instructions to LLMs. By expressing logic as pseudo-code, you leverage the model's extensive training on code to achieve more precise, structured outputs.

This technique provides structural clarity through familiar programming constructs like if/else, loops, and functions.

Key Concepts

  • Control flow: Using if/else, loops, and conditionals
  • Function-like structure: Defining inputs, processing, and outputs
  • Comments as guidance: Using code comments to explain intent
  • Variable placeholders: Clear markers for dynamic content

Examples

Conditional Logic
Decision Tree Style
function classify_email(email): # Check for spam indicators if contains_suspicious_links(email) OR all_caps_subject(email): return "SPAM" # Check urgency if contains_words(["urgent", "asap", "immediately"]): return "HIGH_PRIORITY" # Default classification return "NORMAL" Apply this logic to: {user_email}
Processing Pipeline
Step-by-Step Algorithm
def analyze_sentiment(text): # Step 1: Extract key phrases phrases = extract_phrases(text) # Step 2: Score each phrase (-1 to 1) scores = [score_sentiment(p) for p in phrases] # Step 3: Calculate weighted average final_score = weighted_average(scores) # Step 4: Map to category if final_score > 0.3: return "positive" elif final_score < -0.3: return "negative" else: return "neutral"

Interactive Exercise

Write Pseudo-Code Prompt

Write a pseudo-code prompt for a task that:

1. Takes a product review as input
2. Extracts pros and cons
3. Gives an overall rating

Pro Tips
  • Use familiar programming constructs (if/else, for, while)
  • Add comments to clarify intent
  • Define clear input/output types
  • Works especially well for classification and decision tasks

Related Terms