From the LLM Bootcamp lectures by UC Berkeley PhD alumni, I’ve learned key rules and methods that improve prompts. This article covers techniques like Active Prompting, Meta Prompting, and Few-Shot Prompting etc designed to make models think more systematically and produce better results.
Essential Rules for Writing Effective Prompts
Here are a few important rules that can make prompts more effective. These techniques improve the accuracy and usefulness of responses.
Use Structured Text
Language models perform better when given well-organized input. Just like humans find clear instructions easier to follow, models also work better with structured prompts.
Example: Asking for a Python Function
❌ Without Structure:
"Write a Python function that checks if a number is prime."
✅ With Structure (Pseudocode):
Define a function is_prime(n):
If n is less than 2, return False
For each number i from 2 to sqrt(n):
If n is divisible by i, return False
Return True
A structured prompt makes it easier for the model to understand and follow specific steps, reducing mistakes.
Break Down Complex Requests (Decomposition)
When asking a model to complete a big task, it’s better to split it into smaller steps. This improves accuracy and keeps responses more focused.
Example: Writing a Blog Post
❌ All-in-One Prompt:
"Write a blog post about climate change and include recent statistics."
✅ Step-by-Step Approach:
Gather facts: "List recent climate change statistics from 2023."
Plan the content: "Create an outline for a climate change blog post."
Write in parts: "Write an engaging introduction for a climate change blog post."
Expand each section separately.
Breaking down the task ensures each part is handled properly before moving to the next.
✅ With Structure (Pseudocode):
Define a function is_prime(n):
If n is less than 2, return False
For each number i from 2 to sqrt(n):
If n is divisible by i, return False
Return True
Guide the Model’s Reasoning (Chain of Thought)
Instead of asking for a direct answer, guiding the model through logical steps leads to better responses.
Example: Checking for a Prime Number
❌ Simple Prompt:
"Is 2,345 a prime number?"
✅ Step-by-Step Prompt:
"Let’s determine if 2,345 is a prime number by checking divisibility step by step."
This approach encourages the model to explain its reasoning, making the response more reliable.
Combine Multiple Responses (Ensembling)
No model is perfect. Each has strengths and weaknesses, so combining multiple responses can lead to better accuracy.
For example, instead of relying on a single model to summarize an article, you can:
- Ask two different models and compare results.
- Run the same model multiple times and merge the best answers. This method helps filter out inconsistencies and improves response quality.
Different types of Prompting Methods
Active Prompting
Active Prompting is a method that helps language models improve their reasoning by selecting the most useful examples for learning. Instead of relying on a fixed set of pre-written prompts, this approach identifies uncertain or ambiguous questions and refines them through human annotation.
How Active Prompting Works
The process involves four key steps:
- Uncertainty Estimation – The model answers a set of training questions multiple times (e.g., five attempts). If the answers vary significantly, the question is marked as uncertain.
- Selection – The most uncertain questions are chosen for human review.
- Annotation – Humans provide step-by-step reasoning for these selected questions.
- Inference – The newly annotated examples are added back into the system, improving the model’s accuracy for similar tasks. #### Why Active Prompting Matters
Continue reading the complete article here: https://journal.hexmos.com/dont-let-your-llm-hallucinate-check-out-these-prompting-rules-and-methods/
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