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LearnImplementation GuidesPrompt Engineering Best Practices: Master the Art of AI Communication
intermediate
14 min read
20 January 2025

Prompt Engineering Best Practices: Master the Art of AI Communication

Learn proven techniques for writing effective prompts that consistently produce high-quality results from LLMs. Includes practical examples, templates, and testing strategies for production applications.

Clever Ops AI Team

Prompt engineering is the most accessible yet most impactful skill for working with large language models. While model architectures and training are the domain of AI researchers, anyone can learn to write prompts that unlock 10x better performance from the same model.

The difference between a mediocre prompt and an excellent one can be dramatic: a poorly worded prompt might produce inconsistent, unreliable results 50% of the time, while a well-engineered prompt can achieve 95%+ accuracy on the same task. This isn't about luck or trial-and-error - it's about understanding how LLMs process instructions and applying proven techniques.

In this comprehensive guide, you'll learn the fundamental principles of effective prompts, master advanced techniques like few-shot learning and chain-of-thought reasoning, and build a systematic approach to testing and refining prompts for production use. Whether you're building chatbots, content generators, data extractors, or any AI application, these skills are essential.

Key Takeaways

  • Effective prompts have six characteristics: clear, contextually rich, format-defined, example-driven, constraint-aware, and testable
  • Few-shot learning (2-5 examples) provides 80% of the benefit for most tasks - choose examples that cover edge cases and show exact formatting
  • Chain-of-thought prompting improves accuracy on reasoning tasks by 30-50% by asking the model to show its work step-by-step
  • Advanced techniques like role prompting, self-consistency, and ReAct can further improve outputs for specialized applications
  • Build comprehensive test datasets covering happy paths (30%), edge cases (40%), and adversarial inputs (30%) to properly evaluate prompts
  • A/B test prompt versions in production with 10% traffic before full rollout, tracking accuracy, user satisfaction, and completion rates
  • Treat prompts like code: use version control, document changes, test systematically, and iterate based on production data

Prompt Engineering Fundamentals

Before diving into advanced techniques, let's establish the core principles that make prompts effective.

What Makes a Good Prompt?

A well-engineered prompt has six essential characteristics:

  • Clear and specific: Unambiguous instructions that leave no room for misinterpretation
  • Contextually rich: Provides relevant background information and constraints
  • Format-defined: Specifies exactly how the output should be structured
  • Example-driven: Shows the model what good outputs look like
  • Constraint-aware: Explicitly states what NOT to do
  • Testable: Produces outputs you can reliably evaluate

The Anatomy of an Effective Prompt

Most production prompts follow this structure:

Prompt Template Structuretext
[ROLE/CONTEXT]
You are an expert customer service assistant for TechCorp,
a B2B software company specializing in project management tools.

[TASK]
Analyze the customer's message and provide a helpful, professional response.

[CONSTRAINTS]
- Keep responses under 150 words
- Always acknowledge the customer's concern
- Never promise features that don't exist
- Escalate to human if you detect anger or complex technical issues

[FORMAT]
Response format:
- Acknowledgment: [Brief empathy statement]
- Solution: [Steps or information]
- Next steps: [What the customer should do]

[EXAMPLES]
Example 1:
Customer: "I can't export my data to CSV"
Response:
Acknowledgment: I understand how frustrating export issues can be.
Solution: To export to CSV: 1) Click Reports, 2) Select your data, 3) Choose "Export" → "CSV"
Next steps: Try these steps and let me know if you need further assistance.

[INPUT]
Customer message: {{user_message}}

[OUTPUT]

This structure ensures consistency and quality across diverse inputs.

Common Prompt Anti-Patterns

Avoid these mistakes that plague poorly-engineered prompts:

❌ Vague instructions:

text
Summarize this article.

✅ Specific instructions:

text
Summarize this article in 3 bullet points, each under 20 words, focusing on key business implications for Australian SMEs.

❌ Implicit expectations:

text
Extract the important information from this email.

✅ Explicit format:

text
Extract from this email:
- Sender name
- Main request or question
- Deadline (if mentioned)
- Priority level (High/Medium/Low based on urgency)

Format as JSON.

❌ Assuming context:

text
Is this a good idea?

✅ Providing context:

text
As a cybersecurity expert reviewing this proposed authentication system for a fintech app handling sensitive financial data, evaluate whether this approach meets industry security standards. Consider: data encryption, multi-factor authentication, compliance requirements (APRA CPS 234), and potential vulnerabilities.

Few-Shot Learning: Teaching by Example

Few-shot learning is one of the most powerful prompt engineering techniques. Instead of just describing what you want, you show the model examples of correct outputs.

Zero-Shot vs. Few-Shot vs. Many-Shot

Method Examples Best For Effectiveness
Zero-shot 0 examples, just instructions Simple, well-defined tasks Baseline performance
Few-shot 1-5 examples Most tasks - optimal cost/performance balance 80% of benefit with minimal examples
Many-shot 10+ examples Complex, nuanced tasks with subtle distinctions Marginal gains, higher cost

For most tasks, 2-5 well-chosen examples provide 80% of the benefit.

Choosing Effective Examples

Your examples should:

  • Cover edge cases: Include tricky, ambiguous cases, not just easy ones
  • Show format precisely: Examples define the output format more than instructions
  • Represent diversity: Span the range of inputs you'll encounter
  • Be realistic: Use actual data, not synthetic toy examples

Practical Few-Shot Example: Data Extraction

Let's extract structured data from unstructured text:

Meeting Details Extractiontext
Extract meeting details from messages. Output as JSON.

Example 1:
Input: "Let's meet next Tuesday at 2pm in Conference Room B to discuss Q4 planning. Sarah and Mike should join."
Output:
{
  "date": "next Tuesday",
  "time": "2pm",
  "location": "Conference Room B",
  "topic": "Q4 planning",
  "attendees": ["Sarah", "Mike"]
}

Example 2:
Input: "Quick sync tomorrow morning? 9am works for me."
Output:
{
  "date": "tomorrow",
  "time": "9am",
  "location": null,
  "topic": "quick sync",
  "attendees": []
}

Example 3:
Input: "Can we reschedule our Friday budget review? Something urgent came up."
Output:
{
  "date": "Friday (reschedule requested)",
  "time": null,
  "location": null,
  "topic": "budget review",
  "attendees": []
}

Now extract from this message:
"Team standup Monday 10:30am via Zoom. John, Lisa, and Tom please join to discuss the client deliverables."

Output:

The examples show the model how to handle missing information, informal language, and different formats.

Dynamic Few-Shot Selection

For advanced applications, select examples dynamically based on the input:

Dynamic Example Selectionpython
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

def get_relevant_examples(query, example_pool, embeddings, top_k=3):
    """Retrieve most similar examples for few-shot prompting."""
    # Get query embedding
    query_embedding = embed_text(query)

    # Calculate similarity to all examples
    similarities = cosine_similarity([query_embedding], embeddings)[0]

    # Get top-k most similar
    top_indices = np.argsort(similarities)[-top_k:][::-1]

    return [example_pool[i] for i in top_indices]

# Build prompt with dynamically selected examples
relevant_examples = get_relevant_examples(user_query, example_database, example_embeddings)
prompt = build_few_shot_prompt(relevant_examples, user_query)

This ensures examples are always relevant to the current input, improving performance on diverse tasks.

Chain-of-Thought Prompting

Chain-of-thought (CoT) prompting dramatically improves performance on reasoning tasks by asking the model to show its work.

What is Chain-of-Thought?

Instead of asking for just the final answer, you prompt the model to explain its reasoning step-by-step. This:

  • Improves accuracy on complex reasoning tasks (math, logic, analysis)
  • Makes outputs more explainable and trustworthy
  • Reduces hallucinations by forcing explicit reasoning
  • Enables you to identify where reasoning fails

Basic Chain-of-Thought Example

Without CoT:

text
Q: A store has 15 apples. They sell 6 apples and then receive a delivery of 8 apples. How many apples do they have?
A: 17 apples

With CoT:

text
Q: A store has 15 apples. They sell 6 apples and then receive a delivery of 8 apples. How many apples do they have?

Let me think through this step by step:
1. Starting amount: 15 apples
2. After selling 6: 15 - 6 = 9 apples
3. After receiving 8: 9 + 8 = 17 apples

A: The store has 17 apples.

The second approach is more reliable and verifiable.

Implementing CoT in Production

Add "think step by step" instructions to your prompts:

Support Ticket Analysis with CoTtext
Analyze this customer support ticket and determine:
1. Issue category (Billing / Technical / Account / Other)
2. Priority level (Critical / High / Medium / Low)
3. Suggested assignment (Support / Engineering / Finance)

Ticket: "I was charged twice for my subscription this month. This is the third billing error in two months. I need a refund immediately and I'm considering canceling."

Think through this step by step:

Step 1: Identify the core issue
- Customer was double-charged
- This is a recurring problem (third error)
- Customer is frustrated and considering leaving

Step 2: Categorize
- Primary category: Billing (double charge)
- Secondary aspect: Account (retention risk)

Step 3: Assess priority
- Financial impact: Moderate (double charge on one account)
- Customer sentiment: High frustration, retention risk
- Recurrence: Pattern of errors suggests systemic issue
- Priority: HIGH (not critical as service still works, but urgent due to retention risk)

Step 4: Determine assignment
- Requires immediate billing investigation
- May need Finance for refund processing
- Support should handle customer communication
- Suggested assignment: Support (primary) + Finance (secondary)

Final Answer:
Category: Billing
Priority: High
Assignment: Support + Finance escalation
Reasoning: Double charge with retention risk requires immediate attention from both Support (customer relationship) and Finance (refund processing).

Zero-Shot CoT vs. Few-Shot CoT

Zero-Shot CoT: Simply add "Let's think step by step" or "Let's approach this systematically"

text
Question: If a company's revenue grew by 20% in Q1 and 15% in Q2, what is the total growth for H1?

Let's think step by step:

Few-Shot CoT: Provide examples with reasoning shown

text
Question: If a company's revenue was $100M and grew by 10% in Q1, what's the Q1 revenue?

Let's think step by step:
1. Starting revenue: $100M
2. Growth amount: 10% of $100M = $10M
3. Q1 revenue: $100M + $10M = $110M

Answer: $110M

Question: If a company's revenue grew by 20% in Q1 and 15% in Q2, what is the total growth for H1?

Let's think step by step:

When to Use Chain-of-Thought

Use Case Recommend CoT? Reason
Mathematical reasoning and calculations ✅ Yes Forces step-by-step logic, catches errors
Multi-step decision making ✅ Yes Makes reasoning transparent and auditable
Complex classifications with multiple criteria ✅ Yes Helps model consider all factors systematically
Tasks requiring audit trails ✅ Yes Provides verifiable reasoning path
Accuracy-critical tasks (speed less important) ✅ Yes Improves accuracy at cost of latency
Simple classifications (sentiment, spam detection) ❌ No Overkill - adds latency without benefit
Direct information retrieval ❌ No No reasoning required
Template filling or formatting ❌ No Mechanical task, no complex logic
Latency-critical applications ❌ No CoT adds 30-50% latency overhead

Advanced Prompt Engineering Techniques

Beyond few-shot and chain-of-thought, several advanced techniques can further improve prompt effectiveness.

1. Role Prompting

Assign the model a specific role or persona to bias outputs toward desired expertise:

text
You are a senior cybersecurity analyst with 15 years of experience in financial services. You specialize in threat detection and incident response.

Analyze this security log entry and determine if it represents a potential threat:
[log entry]

Consider: attack patterns, normal vs. anomalous behavior, and severity.

Role prompting works because models are trained on diverse internet text, including domain-specific content. Asking it to adopt an expert role biases its responses toward that domain's knowledge and reasoning patterns.

2. Constrained Generation

Explicitly state what the model should NOT do:

text
Generate a professional email response to this customer complaint.

MUST include:
- Acknowledgment of their concern
- Clear next steps
- Timeline for resolution

MUST NOT include:
- Apologies that admit fault or liability
- Promises of specific outcomes we can't guarantee
- Technical jargon the customer won't understand
- Generic "we value your feedback" statements

3. Output Formatting with Structured Prompts

For extracting structured data, specify exact JSON schema:

Product Extraction Schemajson
Extract product information from this description.

Output must be valid JSON matching this schema:
{
  "name": string,
  "price": number,
  "currency": string,
  "features": string[],
  "inStock": boolean,
  "category": string
}

Description: "The ErgoChair Pro is our premium office chair, featuring lumbar support, adjustable armrests, and breathable mesh. Currently available for $499 AUD. Perfect for home offices."

JSON:

Many modern models support JSON mode natively - enable it for guaranteed valid JSON:

JSON Mode API Examplespython
# OpenAI
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
    response_format={"type": "json_object"}
)

# Anthropic Claude
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": prompt}],
    # Include "output valid JSON" in your prompt for best results
)

4. Self-Consistency

Generate multiple responses and use majority voting or consistency checks:

python
async def get_consistent_answer(prompt, num_samples=5):
    """Generate multiple responses and return most consistent answer."""
    responses = []

    # Generate multiple completions
    for _ in range(num_samples):
        response = await llm.complete(prompt, temperature=0.7)
        responses.append(response)

    # Extract final answers
    answers = [extract_answer(r) for r in responses]

    # Return most common answer
    from collections import Counter
    return Counter(answers).most_common(1)[0][0]

This is particularly effective for tasks where you need high reliability and can afford extra API calls.

5. ReAct (Reasoning + Acting)

Combine reasoning with action-taking for complex workflows:

text
You can use these tools:
- SEARCH[query]: Search the knowledge base
- CALCULATE[expression]: Perform calculations
- EMAIL[recipient, message]: Send an email

Solve this task by alternating between Thought, Action, and Observation.

Task: A customer is asking about their refund status for order #12345.

Thought: I need to look up the order status first.
Action: SEARCH[order #12345 status]
Observation: Order #12345 was refunded on 2025-01-15, $156.99 to card ending in 4532.

Thought: I have the refund details. The refund was processed 5 days ago. I should explain refund processing times.
Action: SEARCH[credit card refund processing time]
Observation: Credit card refunds typically take 5-10 business days to appear.

Thought: I now have all information needed to respond.
Action: EMAIL[customer, "Your refund of $156.99 for order #12345 was processed on Jan 15. It typically takes 5-10 business days to appear on your card ending in 4532. If you don't see it by Jan 27, please contact us."]
Observation: Email sent successfully.

Task complete.

6. Prompt Chaining

Break complex tasks into multiple sequential prompts:

Prompt Chaining Examplepython
# Step 1: Extract key information
prompt1 = "Extract all dates, people, and action items from this meeting transcript: {transcript}"
extraction = llm.complete(prompt1)

# Step 2: Summarize decisions
prompt2 = f"Based on these extracted details: {extraction}, summarize the key decisions made."
summary = llm.complete(prompt2)

# Step 3: Generate action items
prompt3 = f"Based on this summary: {summary}, create a formatted list of action items with owners and deadlines."
action_items = llm.complete(prompt3)

# Step 4: Draft follow-up email
prompt4 = f"Write a professional follow-up email summarizing these action items: {action_items}"
email = llm.complete(prompt4)

Each step produces cleaner outputs than trying to do everything in one complex prompt.

Testing and Refinement Strategies

Writing prompts is iterative. Systematic testing and refinement separates amateur prompt engineering from production-ready systems.

Building a Test Dataset

Create a diverse set of test cases covering:

  • Happy path: Typical, straightforward inputs (30% of tests)
  • Edge cases: Unusual but valid inputs (40% of tests)
  • Adversarial cases: Tricky, ambiguous, or problematic inputs (30% of tests)
python
test_cases = [
    # Happy path
    {
        "input": "I need to return a product I bought last week.",
        "expected_category": "Returns",
        "expected_priority": "Medium",
        "expected_sentiment": "Neutral"
    },
    # Edge case
    {
        "input": "Can I return something I bought 6 months ago but never opened?",
        "expected_category": "Returns",
        "expected_priority": "Low",
        "expected_sentiment": "Neutral",
        "notes": "Outside normal return window"
    },
    # Adversarial
    {
        "input": "Your product broke and now my business is losing $10,000 a day!!! I want a full refund AND compensation!!!",
        "expected_category": "Returns",
        "expected_priority": "Critical",
        "expected_sentiment": "Very Negative",
        "notes": "High emotion, potential legal threat"
    }
]

Automated Prompt Evaluation

Build a testing harness to compare prompt versions:

python
def evaluate_prompt(prompt_template, test_cases):
    """Evaluate a prompt against test cases."""
    results = {
        "correct": 0,
        "total": len(test_cases),
        "failures": []
    }

    for test in test_cases:
        # Generate prompt from template
        prompt = prompt_template.format(input=test["input"])

        # Get model response
        response = llm.complete(prompt)

        # Parse response
        parsed = parse_response(response)

        # Check correctness
        is_correct = (
            parsed["category"] == test["expected_category"] and
            parsed["priority"] == test["expected_priority"]
        )

        if is_correct:
            results["correct"] += 1
        else:
            results["failures"].append({
                "input": test["input"],
                "expected": test,
                "actual": parsed
            })

    results["accuracy"] = results["correct"] / results["total"]
    return results

# Compare prompt versions
version_a_results = evaluate_prompt(prompt_v1, test_cases)
version_b_results = evaluate_prompt(prompt_v2, test_cases)

print(f"Version A accuracy: {version_a_results['accuracy']:.1%}")
print(f"Version B accuracy: {version_b_results['accuracy']:.1%}")

A/B Testing Prompts in Production

Gradually roll out new prompt versions:

python
import random

def get_prompt_version(user_id):
    """Route users to prompt versions for A/B testing."""
    # Deterministic assignment based on user_id
    if hash(user_id) % 100 < 10:  # 10% of users
        return "prompt_v2_experimental"
    else:
        return "prompt_v1_stable"

def process_request(user_id, user_input):
    """Process request with A/B tested prompts."""
    prompt_version = get_prompt_version(user_id)

    # Load appropriate prompt
    prompt_template = load_prompt(prompt_version)
    prompt = prompt_template.format(input=user_input)

    # Get response
    response = llm.complete(prompt)

    # Log for analysis
    log_interaction(
        user_id=user_id,
        prompt_version=prompt_version,
        input=user_input,
        output=response,
        timestamp=now()
    )

    return response

Track metrics like user satisfaction, task completion rate, and response accuracy to determine winners.

Iterative Refinement Process

  1. Start with a baseline prompt - Simple, clear instructions
  2. Test on diverse inputs - Identify failure modes
  3. Analyze failures - What patterns cause problems?
  4. Refine prompt - Add constraints, examples, or clarifications
  5. Re-test - Did accuracy improve? Did new issues emerge?
  6. Repeat - Continue until accuracy plateaus

Prompt Versioning and Documentation

Treat prompts like code - use version control:

Prompt Version Control Structurebash
prompts/
├── customer_classification/
│   ├── v1.0_baseline.txt
│   ├── v1.1_added_few_shot.txt
│   ├── v1.2_constrained_output.txt
│   ├── v2.0_cot_reasoning.txt
│   ├── changelog.md
│   └── test_cases.json
├── content_generation/
│   ├── v1.0_baseline.txt
│   └── ...
└── README.md

Document each version with:

  • What changed and why
  • Performance on test set (accuracy, latency, cost)
  • Known limitations
  • Examples of typical outputs

Production-Ready Prompt Templates

Here are battle-tested templates for common business applications.

Template 1: Customer Support Classification

Customer Support Classification Templatetext
You are a customer support routing assistant for {company_name}.

Your task: Analyze customer messages and classify them for routing.

Classification criteria:
- Category: Technical / Billing / Account / Product / General
- Priority: Critical / High / Medium / Low
- Sentiment: Positive / Neutral / Negative / Very Negative
- Requires human: Yes / No

Priority guidelines:
- Critical: System down, data loss, security issue, legal threat
- High: Blocking work, payment issues, angry customer
- Medium: Feature questions, minor bugs, general inquiries
- Low: Feature requests, compliments, simple questions

Output format (JSON):
{
  "category": "...",
  "priority": "...",
  "sentiment": "...",
  "requires_human": true/false,
  "reasoning": "Brief explanation",
  "suggested_response": "Draft response if requires_human=false"
}

Message: {customer_message}

Analysis:

Template 2: Content Generation with Brand Voice

Content Generation Templatetext
You are a content writer for {company_name}.

Brand voice guidelines:
- Tone: {tone} (e.g., "Professional but approachable")
- Audience: {audience} (e.g., "B2B decision makers in Australia")
- Avoid: {avoid} (e.g., "Jargon, hype, excessive exclamation marks")
- Style: {style} (e.g., "Clear, benefit-focused, data-driven")

Task: Write a {content_type} about {topic}.

Requirements:
- Length: {word_count} words
- Include: {must_include} (e.g., "Customer success story, ROI statistics")
- SEO keywords: {keywords} (use naturally, not stuffed)
- Call-to-action: {cta}

Examples of our brand voice:
{example_1}
{example_2}

Now write the content:

Template 3: Data Extraction and Structuring

Data Extraction Templatetext
Extract structured information from the following unstructured text.

Output must be valid JSON matching this exact schema:
{
  "entities": {
    "people": [{"name": string, "role": string}],
    "organizations": [{"name": string, "type": string}],
    "dates": [{"date": string, "context": string}],
    "amounts": [{"value": number, "currency": string, "context": string}]
  },
  "key_facts": [string],
  "action_items": [{"task": string, "owner": string, "deadline": string}],
  "sentiment": "positive" | "neutral" | "negative"
}

Rules:
- Only extract information explicitly stated in the text
- For missing fields, use null
- Dates should be in ISO format (YYYY-MM-DD) if specific, otherwise keep original
- For action items without explicit owner, use "unassigned"

Text:
{input_text}

JSON:

Template 4: Code Review and Analysis

Code Review Templatetext
You are a senior software engineer reviewing code for security, performance, and best practices.

Review criteria:
1. Security vulnerabilities (SQL injection, XSS, authentication issues)
2. Performance problems (N+1 queries, unnecessary loops, memory leaks)
3. Code quality (readability, maintainability, error handling)
4. Best practices for {language} and {framework}

For each issue found, provide:
- Severity: Critical / High / Medium / Low
- Category: Security / Performance / Quality / Style
- Line number(s): Where the issue occurs
- Description: What the problem is
- Recommendation: How to fix it
- Example: Code snippet showing the fix

Code to review:
```{language}
{code}
```

Review:

Template 5: Meeting Summarization

Meeting Summary Templatetext
Summarize this meeting transcript following this structure:

## Meeting Summary
**Date:** {date}
**Attendees:** {attendees}
**Duration:** {duration}

## Key Decisions
[Bullet list of decisions made, with owner if applicable]

## Action Items
| Task | Owner | Deadline | Status |
|------|-------|----------|--------|
| ... | ... | ... | Not Started |

## Discussion Points
[Brief summary of main topics discussed]

## Next Steps
[What happens next, next meeting date if mentioned]

## Risks and Blockers
[Any mentioned risks, blockers, or concerns]

Transcript:
{transcript}

Summary:

Template 6: Sentiment Analysis with Reasoning

Sentiment Analysis Templatetext
Analyze the sentiment of this text with detailed reasoning.

Step 1: Identify sentiment-bearing phrases
List all phrases that indicate positive, negative, or neutral sentiment.

Step 2: Assess overall sentiment
Consider:
- Balance of positive vs. negative language
- Intensity of emotion (mild frustration vs. extreme anger)
- Context and sarcasm
- Mixed sentiments (positive about product, negative about support)

Step 3: Determine final sentiment
Overall: Positive / Neutral / Negative / Very Negative / Very Positive
Confidence: High / Medium / Low

Step 4: Explain reasoning
Why this sentiment? What are the key indicators?

Text: {input_text}

Analysis:

Conclusion

Prompt engineering is both an art and a science. The techniques in this guide - clear structure, few-shot learning, chain-of-thought reasoning, and systematic testing - will dramatically improve your results. But remember that every application is unique, and the best prompt for your use case will come from iterative refinement based on real-world data.

Start simple. Test thoroughly. Refine based on failures. Document your learnings. And treat prompts like production code: version controlled, tested, and continuously improved.

The most important skill in prompt engineering isn't knowing every technique - it's developing a systematic approach to understanding what works and why. Build feedback loops, analyze failures, and always be testing. The difference between a 70% accuracy prompt and a 95% accuracy prompt isn't magic; it's systematic, data-driven iteration.

Your prompts are the interface between your application and the LLM. Invest time in making them excellent, and you'll see that investment repaid many times over in better, more reliable AI systems.

Frequently Asked Questions

How much does prompt quality affect LLM performance?

Should I use GPT-4, Claude, or another model for best results?

How many examples should I include in few-shot prompts?

Does temperature affect prompt effectiveness?

How do I prevent prompt injection attacks?

Can I reuse prompts across different LLM providers?

How do I handle prompts longer than the context window?

Should I include negative examples in few-shot prompts?

How do I make prompts work consistently across different languages?

What tools exist for prompt engineering and testing?

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