🌿 AI Prompt Engineering Guide

Fundamentals of Prompting

Core Principles

• Clarity – Reduces ambiguity and improves interpretation.
• Context – Adds background and improves relevance.
• Constraint – Focuses tone, length, or structure.

Prompt Size Limitations (when reference material was published)

• ChatGPT: GPT-4-turbo supports up to 128k tokens.
• Gemini: Token limits vary, often smaller.
• Best practice: Place the most important information first.

Prompt Patterns and Techniques

Five-Step Prompt Design Framework (T-C-R-E-I)

• A complete prompt framework based on Google’s model: Task, Context, References, Evaluate, Iterate.
• Why use it: Improves prompt clarity, completeness, and relevance.
• Example:
  • Task: “Summarise this report.”
  • Context: “Audience is the executive board.”
  • References: “Match the tone of last quarter's briefing.”
  • Evaluate: “Does it highlight risks and outcomes?”
  • Iterate: “Add bullet points for each key insight.”

Persona Pattern

• Assigns the AI a specific role to adopt in generating the response.
• Why use it: Aligns tone, terminology, and assumptions with the user’s needs.
• Example: “Act as a senior legal advisor assessing risk in this contract.”

Audience Person Pattern

• Defines the target audience for the AI’s output.
• Why use it: Tailors complexity and tone for specific stakeholders.
• Example: “Explain this IT architecture to a non-technical CEO.”

Root Prompts

• Base prompts for common, repeatable tasks.
• Why use it: Saves time and ensures consistency.
• Example: “Create a summary of this article in five professional bullet points.”

Question Refinement Pattern

• Prompts the AI to revise or optimise an existing prompt.
• Why use it: Improves unclear or poorly scoped prompts.
• Example: “Improve this prompt: ‘Write something about our product’.”

Chain of Thought Prompting

• Instructs AI to explain its thinking step-by-step before answering.
• Why use it: Increases transparency and reduces reasoning errors.
• Example: “Explain each step of your logic before recommending a hiring decision.”

Tree of Thought Prompting

• Explores multiple reasoning paths or solution options in parallel.
• Why use it: Enables comparison between approaches.
• Example: “Suggest three pricing strategies and explain the pros and cons of each.”

ReACT Prompting (Reason + Action)

• Combines logical reasoning with specific actions like calculations or data lookup.
• Why use it: Improves realism and precision in decision-making tasks.
• Example: “Calculate ROI for these investments and then suggest which to pursue.”

Cognitive Verifier Pattern

• Asks the AI to assess the accuracy and coherence of its response.
• Why use it: Reduces errors and hallucinations.
• Example: “Review your answer for consistency with these three sources.”

Flipped Interaction Pattern

• Turns the AI into the questioner or evaluator of the user.
• Why use it: Builds user understanding through challenge or testing.
• Example: “Quiz me on stakeholder management techniques using case examples.”

Ask for Input Pattern

• Encourages AI to seek missing details before responding.
• Why use it: Prevents low-quality outputs caused by lack of information.
• Example: “What else do you need to create a detailed training plan?”

Template Pattern

• Provides a standard format for structuring inputs.
• Why use it: Reduces ambiguity and enforces consistency across prompts.
• Example:
• Prompt Template:
  • Topic: [Insert Topic]
  • Audience: [Insert Audience]
  • Format: [Insert Format]
  • Output Constraints: [e.g. Length, Tone]

Output Expansion Pattern

• Instructs the AI to expand on a previous brief output.
• Why use it: Adds detail, examples, or context.
• Example: “Expand the third bullet to include a real-world example.”

Menu Actions Pattern

• Offers the user a list of next steps or content types to generate.
• Why use it: Makes the interaction more adaptive and responsive.
• Example: “Would you like a summary, slide deck, or checklist?”

Game Play Pattern

• Simulates a scenario or interaction using defined rules or roles.
• Why use it: Enhances training, ideation, or rehearsal through immersion.
• Example: “You are a customer with a complaint—simulate a live support interaction.”

Fact Check List Pattern

• Guides the AI to validate all factual claims against known sources.
• Why use it: Reduces misinformation or invented content.
• Example: “Identify every factual claim and confirm it against the report provided.”

Tail Generation Pattern

• Asks the AI to produce variations of the same response in different formats or styles.
• Why use it: Supports audience segmentation, tone testing, or repurposing.
• Example: “Write this product update as an email, a LinkedIn post, and a Slack message.”

Semantic Filter Pattern

• Restricts the AI’s output to specific themes or meanings.
• Why use it: Improves focus and avoids off-topic content.
• Example: “Only include insights relevant to supply chain optimisation.”

Meta Prompting

• Uses AI to help create more effective prompts.
• Why use it: Helps users who are unsure how to phrase a request.
• Example: “Suggest a well-structured prompt to extract insights from meeting transcripts.”

Iteration Techniques: RaHeN Framework

• The RaHeN Framework helps improve underperforming prompts when the T-C-R-E-I structure is not enough.
• Why use it: Offers systematic strategies for refining AI responses.
  • Revisit – Add or adjust task, persona, or context. Example: “Add a persona and context to improve the summary output.”
  • Halve complexity – Break down overly long or dense instructions. Example: “Split the original prompt into two simple instructions.”
  • Narrative reframing – Ask for a story or analogy to clarify the idea. Example: “Explain this as a story about a manager solving a problem.”
  • Set constraints – Add format, style, or topic boundaries. Example: “Rewrite in under 200 words using only financial data.”

See also
🌱 Preventing AI Hallucinations

Sources:
https://youtu.be/p09yRj47kNM?si=Jki8bF2wAr3SNc3T
https://www.coursera.org/learn/prompt-engineering?specialization=prompt-engineering