LLM Learning Portal

Prompt Engineering

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Fundamentals of Prompt Engineering

Prompt engineering is the art and science of crafting inputs to elicit optimal outputs from language models.

Why Prompt Engineering Matters

  • Model Capabilities

    Unlock hidden abilities of models without changing parameters

  • Output Quality

    Dramatically improve relevance, accuracy, and usefulness

  • Task Adaptation

    Guide models to perform specialized tasks without fine-tuning

  • Cost Efficiency

    Reduce token usage by getting better results in fewer attempts

Evolution of Prompting

Basic Prompting (2020-2021)

Simple instructions or questions with limited control

"Write a poem about a cat."

Few-Shot Learning (2021-2022)

Providing examples to demonstrate the expected pattern

"Q: What is 2+2? A: 4
Q: What is 5+7? A: "

Chain-of-Thought (2022-2023)

Instructing models to reason step-by-step

"Let's solve this step by step..."

Advanced Techniques (2023+)

Structured prompting, metaprompting, and role-playing

"You are an expert physicist. Analyze this problem systematically..."

Core Prompting Techniques

Role-Based Prompting

Assigning specific roles or personas to guide model behavior

Example: "You are an experienced data scientist specializing in causal inference..."
Benefits: Activates domain-specific knowledge and response patterns

Few-Shot Learning

Providing examples that demonstrate the desired pattern

Example: "Input: Cold day
Output: Wear a warm jacket
Input: Rainy day
Output: ..."
Benefits: Helps model understand patterns and expected formats

Chain-of-Thought Prompting

Encouraging step-by-step reasoning for complex problems

Example: "Think through this problem carefully. First, identify the variables..."
Benefits: Dramatically improves reasoning tasks and reduces errors

Output Structuring

Specifying exact formats for model responses

Example: "Format your response as a JSON object with the following fields: ..."
Benefits: Makes outputs easier to parse and process programmatically

Advanced Prompt Engineering

The Anatomy of Effective Prompts

1. Clear Context

Provide relevant background and constraints

2. Specific Instructions

Detailed guidance on what to do and how to do it

3. Format Specifications

Explicit structure for the desired output

4. Examples (optional)

Demonstrations of expected input-output pairs

5. Evaluation Criteria

Standards the response should meet

Example Template:

# Context You are a [role] with expertise in [domain]. # Task [Clear description of what needs to be done] # Guidelines - [Specific instruction 1] - [Specific instruction 2] - [Specific instruction 3] # Output Format [Description of how the output should be structured] # Examples Input: [Example input] Output: [Example output]

Advanced Techniques

Tree of Thoughts

Exploring multiple reasoning paths simultaneously

"Let's consider multiple approaches to this problem:

Approach 1: [First reasoning path]

Approach 2: [Second reasoning path]

Now, evaluating each approach..."

ReAct (Reasoning + Acting)

Interleaving reasoning steps with actions

"Thought: I need to find information about X

Action: Search for X

Observation: [search results]

Thought: Based on these results..."

Self-Consistency

Generating multiple answers and selecting the most consistent one

"Let me solve this multiple times and compare my answers:

Solution 1: ...

Solution 2: ...

The most reliable answer is..."

Advanced techniques often require more tokens but can provide significant quality improvements for complex tasks.

Prompt Engineering Case Studies

Code Generation

Key Techniques:

  • Detailed specifications
  • Example code patterns
  • Iterative refinement
50-70% improvement in functional code
Math Problem Solving

Key Techniques:

  • Chain-of-thought
  • Step verification
  • Self-consistency
30-80% reduction in calculation errors
Content Generation

Key Techniques:

  • Structured outlines
  • Persona definition
  • Tone specification
40% increase in relevance metrics
Well-engineered prompts can sometimes match the performance of models that are 10-100x larger in parameter count!