Bonus: Reduce Hallucination Hallucinations happen when models make stuff up. Fix it with: Retrieval Augmented Generation (RAG) – ReAct (reason + act)
– Chain-of-Verification Don’t just ask questions. Ask it to check its own answers.
PROMPT ENGINEERING
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Techniques to Reduce AI Model Hallucinations
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Using Logic-of-Thought to Enhance LLM Reasoning
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10. Expert: Logic-of-Thought (LoT) Inject formal logic. Prompt the model to identify, verify, and reason using rules like: If A implies B, and A is true, then B must be true. Perfect for law, ethics, science, structured thinking.
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Improving AI Model Reasoning with Chain-of-Code Prompting
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9. Expert: Chain-of-Code (CoC) Want precision? Ask the model to reason in pseudocode or actual code. Why? Code forces structure and logic.
It reduces fluff, boosts accuracy. Example: "Write code to solve this step by step…" -
Using Self-Refine Prompting to Improve LLM Output Quality
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8. Advanced: Self-Refine Prompt → Output → Self-Critique → Improved Output Let the model fix itself. Prompt: "Write a tweet. Now critique it. Now rewrite it based on your feedback." This loop improves clarity, tone, and logic.
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Enhancing LLM Reasoning with Graph-of-Thoughts Prompting
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7.Advanced: Graph-of-Thoughts (GoT) Human thought isn’t linear. So why force your prompts to be? GoT lets LLMs combine, backtrack, and remix ideas.
Think of it like brainstorming with memory. Great for creativity, planning, design. -
Understanding Tree-of-Thoughts Prompting for LLMs
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6. Advanced: Tree-of-Thoughts (ToT) Don’t stop at one reasoning path. Explore many, like a decision tree. The model proposes, tests, and chooses from its ideas. It’s how GPT-4 solves riddles, puzzles, strategy games.
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Using Self-Consistency for Reliable LLM Outputs
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5. Intermediate: Self-Consistency Ask the model the same question multiple times. Then pick the most common answer. Why? Because LLMs can vary and the most repeated answer is often the most reliable. Ensemble thinking, but faster.
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Using Auto-CoT for Scalable Reasoning
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4. Intermediate: Auto-CoT Don't want to write examples yourself? Auto-CoT does it for you. Prompt the model to generate its own demos: "Here are a few examples. Let’s think step by step." Now you’ve got scalable reasoning with less effort.
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Improving Model Reasoning with Chain-of-Thought Prompting
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3. Intermediate: Chain-of-Thought (CoT) Make the model "think step-by-step." This boosts reasoning dramatically. Instead of: "What's 13 * 17?" Try: "Let’s solve this step by step." It will explain its thinking before answering.
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Introduction to Few-Shot Prompting for LLMs
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2. Beginner: Few-Shot Prompting Show it examples. Like teaching by demonstration. Prompt:
Q: What’s 5+5?
A: 10
Q: What’s 9+3?
A: 12
Q: What’s 7+2?
A: ? This works because LLMs are pattern matchers.