We’ve learned how to give an AI agent clear instructions and how to define its core personality with a system prompt. But what happens when the task isn’t straightforward? How do we handle problems that require logic, reasoning, and multiple steps to solve?
To build agents that can tackle complex challenges, we need to move beyond simple instructions and adopt techniques that guide the LLM’s thinking process. These advanced methods are the key to unlocking more reliable and accurate reasoning. Let’s explore three of the most powerful techniques in the modern prompt engineering toolkit.
1. Few-Shot Prompting: Learning by Example
The simplest way to improve an agent’s performance on a specific task is to show it what you want. Few-Shot Prompting is the practice of providing several examples of a completed task directly within the prompt, before you ask it to perform a new one.
Why it works: Instead of relying on the LLM to interpret your instructions from scratch (a “zero-shot” prompt), you’re providing a clear pattern to follow. The model uses these examples to learn the desired format, style, and logic in context.
Analogy: It’s like training a new employee. You could just describe a task (zero-shot), or you could show them one finished example (one-shot). But the most effective way is to show them a few completed examples that cover different scenarios (few-shot).
Example: Structured Data Extraction
Zero-Shot Prompt (Less Reliable):
Extract the product name and SKU from this text: "The item for sale is the Hyperion-7 desktop computer, SKU H-78-B1."This might work, but it could fail with slightly different phrasing or formats.
Few-Shot Prompt (More Reliable):
Extract the product name and SKU from the text, following the examples below. If a value is missing, use "N/A".
Text: "Please check the stock for our flagship laptop, the AeroBook Pro (SKU: AB-PRO-2024)."
Product: AeroBook Pro
SKU: AB-PRO-2024
Text: "The Omni-Tool multi-tool is a top seller."
Product: Omni-Tool
SKU: N/A
Text: "Information for the new Quantum-X keyboard, part number QX-KB-003, is now available."
Product: Quantum-X
SKU: QX-KB-003
---
Text: "The item for sale is the Hyperion-7 desktop computer, SKU H-78-B1."
Product:
SKU:The model now understands how to handle different labels (“SKU”, “part number”), missing data, and various sentence structures, making it far more robust.
2. Chain-of-Thought (CoT) Prompting: Showing the Work
For problems that require reasoning, arithmetic, or logical deduction, simply asking for the answer can be a gamble. The LLM might rush to a conclusion and make a simple mistake. Chain-of-Thought (CoT) Prompting solves this by instructing the model to “think step-by-step” and lay out its reasoning process before giving the final answer.
Why it works: It forces the model to slow down and follow a logical sequence. Each step builds on the last, reducing the chance of a critical error. For developers, this also makes the agent’s reasoning transparent and much easier to debug.
Analogy: A math teacher who insists that students “show their work.” The process of arriving at the answer is just as important as the answer itself.
Example: Multi-step Planning Problem
Standard Prompt (Often Fails):
I need to make 10 gift baskets.
Each basket needs 2 apples, 3 chocolates, and 1 notebook.
I have 15 apples, 40 chocolates, and 5 notebooks in stock.
How many more of each item do I need?An LLM might miscalculate one of the items or mix up the numbers.
CoT Prompt (Often Succeeds):
I need to make 10 gift baskets.
Each basket needs 2 apples, 3 chocolates, and 1 notebook.
I have 15 apples, 40 chocolates, and 5 notebooks in stock.
Calculate how many more of each item I need.
Let's think step-by-step.By adding that simple final sentence (Let's think step-by-step), the model is prompted to produce a thought process like this:
First, I need to calculate the total number of each item required for 10 baskets.
Total apples needed: 10 baskets * 2 apples/basket = 20 apples.
Total chocolates needed: 10 baskets * 3 chocolates/basket = 30 chocolates.
Total notebooks needed: 10 baskets * 1 notebook/basket = 10 notebooks.
Next, I will compare the needed amounts to the amounts in stock.
Apples: I need 20 but have 15. So I need 20 – 15 = 5 more apples.
Chocolates: I need 30 but have 40. So I need 30 – 40 = -10. I have 10 extra and don’t need more.
Notebooks: I need 10 but have 5. So I need 10 – 5 = 5 more notebooks.
Final Answer: You need 5 more apples and 5 more notebooks. You have a surplus of 10 chocolates.
3. Self-Consistency: The Wisdom of the Crowd
Self-Consistency is a more advanced technique that builds directly on Chain-of-Thought to achieve even higher accuracy. The core idea is that there may be several ways to reason about a problem, and not all paths lead to the right answer. By exploring multiple paths, you can find the most reliable conclusion.
Why it works: It leverages a simple statistical truth: while a model might make a random error on one reasoning path, it’s less likely to make the same error across many different attempts. The correct answer is often the one that the model arrives at most frequently from different lines of reasoning.
Analogy: Consulting a committee of experts. You don’t just rely on one opinion. You ask several experts to solve the problem independently and then take the consensus answer.
How it works conceptually:
Using the same gift basket problem, you would run the CoT prompt multiple times, getting slightly different reasoning paths.
- Reasoning Path 1 → Correctly calculates you need 5 apples, 0 chocolates, 5 notebooks.
- Reasoning Path 2 → Makes a mistake, calculating 10 * 3 = 40 for chocolates, and concludes you need 5 apples, 0 chocolates, 5 notebooks. (The final answer is incidentally correct, but the reasoning was flawed).
- Reasoning Path 3 → Correctly calculates everything and concludes you need 5 apples, 0 chocolates, 5 notebooks.
- Reasoning Path 4 → Misreads the number of notebooks in stock as 10 and concludes you need 5 apples, 0 chocolates, 0 notebooks.
- Reasoning Path 5 → Correctly calculates everything and concludes you need 5 apples, 0 chocolates, 5 notebooks.
The self-consistency method looks at the final answers. The conclusion “need 5 apples, 0 chocolates, 5 notebooks” appears 4 out of 5 times. This is selected as the most trustworthy result, effectively filtering out the error from Path 4.
Conclusion: Building a More Methodical Mind
These advanced techniques are about adding structure and rigor to the LLM’s “thought” process. They move us beyond simply asking for information and toward guiding a methodical problem-solving process. By using Few-Shot examples to provide context, Chain-of-Thought to enforce logical steps, and Self-Consistency to verify the conclusion, you can build agents that are not just clever, but genuinely more reliable and intelligent.
Author
Experienced Cloud & DevOps Engineer with hands-on experience in AWS, GCP, Terraform, Ansible, ELK, Docker, Git, GitLab, Python, PowerShell, Shell, and theoretical knowledge on Azure, Kubernetes & Jenkins. In my free time, I write blogs on ckdbtech.com