🌿 Fundamentals of Prompting

See also Claude prompting protocol

Core Principles

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

Best practice

• Place the most important information first.
• Be specific about what's required (see framework below)

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.”

Role

• 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

• 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.”

Question Refinement

• 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

• 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

• 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?

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.”

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.”

See also
🌱 Preventing AI Hallucinations

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