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advanced prompting

This section walks you through three additional high impact patterns:

  • Chain of thought prompts
  • Few shot learning prompts
  • Meta cognitive prompts

The idea behind these techniques is to unlock deeper reasoning, precision, and adaptability from any LLM. Let’s walk through them:

Chain of thought (COT) prompts:

This pattern explicitely asks the model to ‘think out load’ - breaking complex reasoning into logical step by step segments.

It is important to note here, that you can do this manually, or in a way - use the ‘thinking’ models in various LLM products to simulate the same thing.

Why this method is so powerful:

Why it works:

  • Mirrors how humans decompose problems.
  • Encourages the model to surface intermediate steps, reducing leaps and hallucinations.
  • Boosts accuracy on multi-step tasks (math, code logic, strategy).

How to use it:

“lets think through this step by step:

  1. First, identify…
  2. Next, determine…
  3. Then, calculate…
  4. Finally, conclude…”

Example:

Prompt: “You’re a data analyst. I have a dataset showing monthly sales. Let’s think step-by-step to identify the three biggest seasonal trends and explain why they occur.”

Few shot learning prompts

This prompting technique provides a handful of input–output examples within the prompt so the model infers the desired pattern.

Why it works:

  • Anchors the model in your task’s format and style.
  • Requires no fine tuning and offers instant customisation.
  • Particularly effective for translation, classification, format transformation.

How to use it:

“Convert these product descriptions into Twitter-style blurbs: Example 1: Input: ‘A 12oz stainless steel travel mug…’ Output: ‘Stay caffeinated on the go ☕️✨ Leak-proof steel mug that fits cup holders. #TravelEssentials’ Example 2: Input: ‘Noise-cancelling wireless earbuds…’ Output: ‘Silence the world, hear the beat 🎧🔇 30-hour battery, crystal sound. #MusicLovers’ Now convert: ‘Ergonomic office chair with lumbar support…’” Tips:

  • Use 2–5 high-quality, diverse examples.
  • Match examples to your actual data in tone, length, and structure.
  • If performance lags, swap or augment examples.

Meta cognitive prompts

This technique creates prompts that ask the model to reflect on how it will solve a problem before providing the answer—i.e., “thinking about its own thinking.”

Why it works:

  • Checks the model’s grasp of the task before execution.
  • Surfaces hidden assumptions and clarifies ambiguous requests.
  • Can improve both correctness and creativity.

How to use it:

“Before answering, please outline: 1. The key information you need. 2. Potential pitfalls or ambiguous areas. 3. The steps you’ll take to ensure accuracy. Then, provide the final answer.” Example:

Prompt: “You’re an AI tutor. First, list what you need to teach someone the Pythagorean theorem clearly (e.g., prerequisites, examples). Then deliver the lesson in three paragraphs.”

Combining patterns is even more powerful

For maximum effect, layer them:

“You’re a security analyst. Let’s think step-by-step how to secure a web app (CoT). Here are two samples of security checklists (Few-Shot). Next, reflect on any assumptions you’re making and list them (Meta-Cognitive). Finally, generate a consolidated, prioritized security plan.”

Conclusion

  • Chain of thought unlocks deep reasoning.
  • Few shot enforces format and tone.
  • Meta cognitive prevalidates approach and reduces errors.

Master these patterns and you’ll transform prompts from simple queries into powerful, reliable AI workflows.