Advanced Prompt Engineering: Beyond the Basics

Move beyond simple prompts to build reliable, production-grade AI applications.

Foundational Techniques

Chain-of-Thought (CoT)

Force step-by-step reasoning:

Question: If a store has 234 apples and sells 89,
then receives 156 more, how many does it have?

Without CoT: 301 (may be wrong)

With CoT:
"Let me think step by step:
1. Starting apples: 234
2. After selling 89: 234 - 89 = 145
3. After receiving 156: 145 + 156 = 301

The store has 301 apples."

Few-Shot Learning

Provide examples:

Task: Classify customer feedback sentiment

Example 1:
Feedback: "Great product, fast shipping!"
Sentiment: Positive

Example 2:
Feedback: "Item arrived broken, terrible experience"
Sentiment: Negative

Now classify:
Feedback: "Okay quality but overpriced"
Sentiment:

Role Prompting

Set context and expertise:

You are an expert financial analyst with 20 years
of experience in equity research. You specialize
in technology companies and valuation methodologies.

Analyze the following earnings report...

Advanced Techniques

Self-Consistency

Multiple reasoning paths for reliability:

Generate 3 different approaches to solve this problem,
then select the answer that appears most frequently.

Structured Output

Enforce specific formats:

Respond only in valid JSON with this structure:
{
  "summary": "string, max 100 words",
  "key_points": ["array of 3-5 strings"],
  "sentiment": "positive|neutral|negative",
  "confidence": "number 0-1"
}

Prompt Chaining

Break complex tasks into steps:

Step 1: Extract key entities from the document
Step 2: Summarize the main argument
Step 3: Identify supporting evidence
Step 4: Generate final analysis

Reliability Patterns

Guardrails

Add constraints to prevent issues:

Instructions:
- Only use information from the provided context
- If uncertain, say "I don't have enough information"
- Never generate harmful or inappropriate content
- Cite specific sources when making claims

Error Handling

Build in recovery:

If you cannot complete the task:
1. Explain why you cannot proceed
2. Suggest what additional information would help
3. Offer an alternative approach if available

Validation

Verify outputs:

After generating your response:
1. Check that all required fields are present
2. Verify numbers and calculations
3. Confirm sources are cited
4. Flag any uncertainties

Domain-Specific Prompts

Code Generation

You are a senior software engineer writing production code.

Requirements:
- Use {language} best practices
- Include error handling
- Add comprehensive comments
- Write unit tests
- Consider edge cases

Generate code for: {specification}

Data Analysis

Analyze this dataset as an experienced data scientist:

1. Describe the data structure
2. Identify key patterns and outliers
3. Suggest relevant visualizations
4. Provide actionable insights
5. Note any data quality issues

Content Creation

You are a content strategist for {industry}.

Create content that:
- Matches brand voice: {description}
- Targets audience: {persona}
- Achieves goal: {objective}
- Follows format: {specifications}

Optimization Techniques

Token Efficiency

Reduce costs without losing quality:

❌ Verbose:
"Please provide a detailed and comprehensive analysis
of the following text, making sure to cover all
important aspects and provide thorough explanations..."

✓ Concise:
"Analyze this text. Cover: key themes, sentiment,
and recommendations."

Context Management

Handle long contexts:

The following document sections are provided in order
of relevance. Focus primarily on Section 1 and 2.

[SECTION 1: Most relevant]
...

[SECTION 2: Supporting context]
...

[SECTION 3: Background only]
...

Caching Strategies

Reuse common prompts:

System prompt (cached):
[Standard instructions, role, format requirements]

User prompt (variable):
[Specific task with user input]

Testing and Evaluation

Prompt Testing

For each prompt variation, test with:
- Edge cases
- Adversarial inputs
- Boundary conditions
- Multi-turn interactions

Quality Metrics

Metric	How to Measure
Accuracy	Ground truth comparison
Consistency	Same input variance
Latency	Response time
Token usage	Input/output tokens

A/B Testing

Test variations systematically:
- Prompt A: Detailed instructions
- Prompt B: Example-based
- Prompt C: Minimal guidance

Measure: accuracy, speed, cost, user preference

Production Considerations

Version Control

Track prompt versions:

prompt_id: user_classification_v3
version: 3.2.1
last_updated: 2026-01-04
author: team_ml
changes: Added edge case handling

Monitoring

Track in production:

• Success rate
• Error patterns
• User feedback
• Model behavior changes

Fallback Strategies

Primary: Claude Opus 4.5
Fallback 1: Claude Sonnet 4
Fallback 2: Simplified prompt with GPT-5.2
Error: Human escalation

Common Pitfalls

Pitfall	Solution
Ambiguous instructions	Be specific and explicit
Missing context	Provide necessary background
Overloaded prompts	Break into smaller tasks
Inconsistent outputs	Add structure and examples
Security issues	Validate inputs, sanitize outputs

---

Need help optimizing your AI prompts? Let’s discuss your use case.