Fine-tuning vs Prompt Engineering

Why I Started Comparing These

I wanted to customize LLM for our service. There were two methods: fine-tuning and prompt engineering. Which should I choose?

Fine-tuning is expensive but performs well, prompt engineering is simple but has limitations. I tried both and understood the difference.

The Confusion

The most confusing part was "when to fine-tune and when to just use prompts?"

Another confusion was "isn't fine-tuning always better?" If you spend cost and time training the model, shouldn't it naturally be better?

And "when is prompt engineering sufficient?" was also unclear.

The 'Aha!' Moment

The decisive analogy was "chef training."

Prompt Engineering = Giving recipe:

• Give detailed recipe (prompt) to chef (LLM)
• Chef already knows how to cook, just follows recipe
• Fast and simple, but depends on chef's basic skills

Fine-tuning = Specialized training:

• Train chef to specialize in specific cuisine (domain)
• Takes time and cost, but becomes expert in that cuisine
• Cooks that cuisine naturally without recipe

This analogy helped me understand. Prompt engineering is fast and flexible, but fine-tuning provides specialized performance for specific tasks.

Prompt Engineering

Core Idea

Without changing the model, design input (prompt) well to get desired results.

# Basic prompt
prompt = "Analyze this review: This product is great!"
response = llm(prompt)

# Improved prompt (Few-shot)
prompt = """
Analyze sentiment of these reviews:

Review: "Best product ever"
Sentiment: positive

Review: "Not good"
Sentiment: negative

Review: "This product is great!"
Sentiment:
"""
response = llm(prompt)  # "positive"

Advantages

1. Quick start: Immediately usable
2. Cost savings: No model training needed
3. Flexibility: Just change prompt
4. No data needed: No training data required

Disadvantages

1. Token limit: Prompts can get too long
2. Lack of consistency: Different outputs for same input
3. Performance limit: Complex tasks are difficult
4. Cost increase: Send long prompt with every API call

Real Usage Example

from openai import OpenAI

client = OpenAI()

# Define role with system prompt
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a friendly customer service chatbot."},
        {"role": "user", "content": "I want a refund"}
    ]
)

print(response.choices[0].message.content)

Fine-tuning

Core Idea

Retrain the model itself for specific tasks. Update existing model weights.

# Prepare training data
training_data = [
    {"prompt": "Review: Great", "completion": "positive"},
    {"prompt": "Review: Bad", "completion": "negative"},
    # ... hundreds to thousands
]

# Fine-tune
fine_tuned_model = finetune(base_model, training_data)

# Use
response = fine_tuned_model("Review: Good")  # "positive"

Advantages

1. High performance: Optimized for specific tasks
2. Consistency: Same output for same input
3. Short prompts: No long explanations needed
4. Domain specialization: Learns terminology, style

Disadvantages

1. Data needed: Hundreds to thousands of training samples
2. Time required: Training takes time
3. Cost: GPU, API costs
4. Less flexible: Need retraining when task changes

Real Usage Example

from openai import OpenAI

client = OpenAI()

# 1. Upload training data
with open("training_data.jsonl", "rb") as f:
    training_file = client.files.create(file=f, purpose="fine-tune")

# 2. Start fine-tuning
fine_tune_job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-3.5-turbo"
)

# 3. Use fine-tuned model
response = client.chat.completions.create(
    model=fine_tune_job.fine_tuned_model,
    messages=[{"role": "user", "content": "Review: Good"}]
)

Key Differences

Feature	Prompt Engineering	Fine-tuning
Start time	Immediate	Hours to days
Cost	API call cost	Training cost + API cost
Data	Not needed	Hundreds to thousands needed
Performance	Medium	High
Consistency	Low	High
Flexibility	High	Low
Maintenance	Easy	Difficult

When to Use What?

Use Prompt Engineering for:

1. Quick prototype: MVP development
2. Various tasks: Handle multiple types of tasks
3. Lack of data: No training data available
4. Frequent changes: Requirements keep changing

# Example: Multi-purpose chatbot
def chatbot(user_input, task_type):
    if task_type == "translation":
        prompt = f"Translate to English: {user_input}"
    elif task_type == "summary":
        prompt = f"Summarize: {user_input}"
    elif task_type == "sentiment":
        prompt = f"Analyze sentiment: {user_input}"
    
    return llm(prompt)

Use Fine-tuning for:

1. Specific domain: Medical, legal, etc.
2. Consistency important: Need same output for same input
3. High volume: Repetitive same task
4. Performance critical: Need highest quality

# Example: Medical diagnosis assistant
# Fine-tune with thousands of medical data
medical_model = finetune(base_model, medical_data)

# Consistent and accurate diagnosis assistance
diagnosis = medical_model("Symptoms: headache, fever, cough")

Real Project Choices

My actual project experiences.

Project 1: Customer Service Chatbot

Goal: Handle various customer inquiries

Choice: Prompt Engineering

Reason:

• Inquiry types are diverse and constantly changing
• Need quick deployment
• Difficult to collect training data

Implementation:

system_prompt = """
You are a friendly customer service chatbot.
- Always use polite language
- Focus on problem solving
- Connect to agent when needed
"""

response = llm(system_prompt + user_query)

Result: Deployed in 2 weeks, 80% customer satisfaction

Project 2: Legal Document Analysis

Goal: Find risky clauses in contracts

Choice: Fine-tuning

Reason:

• Legal terminology accuracy important
• Need consistent analysis
• Have 1000+ labeled contracts

Implementation:

# Fine-tune with 1000 contracts
legal_model = finetune(
    base_model="gpt-3.5-turbo",
    training_data=legal_contracts
)

# Accurate and consistent analysis
risks = legal_model.analyze(contract)

Result: 95% accuracy, 70% reduction in lawyer review time

Project 3: Product Review Sentiment Analysis

Goal: Classify reviews as positive/negative

1st attempt: Prompt Engineering

• Accuracy: 75%
• Cost: $500/month (API calls)

2nd attempt: Fine-tuning

• Trained with 5000 reviews
• Accuracy: 92%
• Cost: Initial $200 + $100/month

Conclusion: Switched to fine-tuning → improved performance + cost savings

Hybrid Approach

Recently, combining both methods is popular.

RAG (Retrieval-Augmented Generation)

Automatically add relevant information to prompt:

# 1. Search relevant documents
relevant_docs = vector_db.search(user_query)

# 2. Include in prompt
prompt = f"""
Reference documents:
{relevant_docs}

Question: {user_query}
Answer:
"""

response = llm(prompt)

Fine-tuning + Prompt

Fine-tune prompt on fine-tuned model:

# Fine-tuned model
fine_tuned_model = load_model("my-fine-tuned-model")

# Fine-tune with prompt
prompt = f"""
Format: JSON
Fields: sentiment, confidence, reason

Review: {review}
"""

response = fine_tuned_model(prompt)

Cost Comparison

Prompt Engineering Cost

# GPT-4 API cost (2024 baseline)
# Input: $0.03 / 1K tokens
# Output: $0.06 / 1K tokens

# Example: 100K requests/month
# Average prompt: 500 tokens
# Average response: 200 tokens

monthly_cost = (
    100_000 * 500 / 1000 * 0.03 +  # Input
    100_000 * 200 / 1000 * 0.06    # Output
) = $1,500 + $1,200 = $2,700

Fine-tuning Cost

# GPT-3.5 fine-tuning cost
# Training: $0.008 / 1K tokens
# Usage: $0.012 / 1K tokens (input + output)

# Initial training cost (one-time)
training_cost = 5_000 * 500 / 1000 * 0.008 = $20

# Monthly usage cost
# Prompts become shorter (100 tokens)
monthly_cost = (
    100_000 * 100 / 1000 * 0.012
) = $120

# Total cost (first month)
total = $20 + $120 = $140

Conclusion: Fine-tuning is much cheaper for high volume!

Practical Tips

1. Start with Prompt Engineering

Try prompts first, consider fine-tuning when hitting limits:

# Step 1: Basic prompt
result = llm("Classify: " + text)

# Step 2: Few-shot prompt
result = llm(few_shot_prompt + text)

# Step 3: Chain-of-Thought
result = llm(cot_prompt + text)

# Step 4: Consider fine-tuning
if accuracy < 90%:
    consider_finetuning()

2. Data Quality is Key

Data quality is most important for fine-tuning:

# Bad example: Noisy data
bad_data = [
    {"prompt": "good", "completion": "positive"},  # Too short
    {"prompt": "bad product!!!", "completion": "neg"},  # Typo
]

# Good example: Clean and consistent data
good_data = [
    {"prompt": "Review: This product is excellent", "completion": "positive"},
    {"prompt": "Review: Not satisfied with quality", "completion": "negative"},
]

3. Gradual Improvement

Don't try to be perfect at once, improve gradually:

# v1: Basic prompt
v1 = basic_prompt(text)

# v2: Add examples
v2 = few_shot_prompt(text)

# v3: Fine-tune with 100 samples
v3 = finetune(model, data_100)

# v4: Fine-tune with 1000 samples
v4 = finetune(model, data_1000)

4. Monitor Performance

Track metrics to understand when to switch from prompts to fine-tuning:

# Track accuracy over time
metrics = {
    'prompt_v1': 0.70,
    'prompt_v2': 0.75,
    'prompt_v3': 0.78,  # Plateauing
    'finetune_v1': 0.92  # Significant jump
}

# Switch when prompt engineering plateaus
if improvement < 0.03:
    switch_to_finetuning()

5. Consider Maintenance Cost

Fine-tuned models need retraining when requirements change. Factor this into your decision.

# Prompt: Easy to update
new_prompt = old_prompt + "\nNew requirement: ..."

# Fine-tuning: Need to retrain
new_model = finetune(base_model, new_training_data)  # Time + cost

Real-World Decision Framework

Here's how I decide between prompt engineering and fine-tuning:

Decision Tree

Start
  ↓
Do you have 500+ labeled examples?
  No → Prompt Engineering
  Yes ↓
Is consistency critical?
  No → Prompt Engineering
  Yes ↓
Will you process 10,000+ requests/month?
  No → Try Prompt Engineering first
  Yes ↓
Is accuracy > 90% required?
  No → Prompt Engineering might suffice
  Yes → Fine-tuning

Cost-Benefit Analysis

# Calculate break-even point
prompt_cost_per_request = 0.027  # $0.027
finetune_cost_per_request = 0.0012  # $0.0012
finetune_training_cost = 20  # $20 one-time

# Break-even at:
requests = finetune_training_cost / (prompt_cost_per_request - finetune_cost_per_request)
# = 20 / 0.0258 = ~775 requests

# If you'll process > 775 requests, fine-tuning is cheaper

Wrapping Up

Prompt engineering and fine-tuning are two main methods for LLM customization. Prompt engineering is fast and flexible but has limits in performance and consistency. Fine-tuning provides high performance and consistency but requires time and cost.

I start most projects with prompt engineering. Quickly build prototype, identify limits through actual use. Then switch to fine-tuning only when really necessary.

The key is "cost-benefit ratio." If prompts are sufficient, no need to fine-tune. But if high volume, high accuracy, and consistency are important, fine-tuning is the answer.

Remember: start simple, measure everything, and scale complexity only when data proves it's necessary. The best solution isn't always the most sophisticated one—it's the one that solves your problem efficiently.

Final Thoughts

In my experience, the decision between prompt engineering and fine-tuning often comes down to three factors: time, data, and scale.

If you're just starting out or building a prototype, prompt engineering is almost always the right choice. It's fast, flexible, and requires no training data. You can iterate quickly and validate your idea before investing in fine-tuning.

But if you're building a production system that will process thousands of requests daily, need consistent high-quality outputs, and have the training data available, fine-tuning will pay for itself quickly through better performance and lower operational costs.

The key is to be pragmatic. Don't fine-tune just because it's trendy. Don't stick with prompts just because they're easier. Measure, analyze, and choose the approach that delivers the best results for your specific situation. Your users will thank you for it.

One final piece of advice: document your decision-making process. Whether you choose prompts or fine-tuning, write down why you made that choice, what metrics you're tracking, and what would trigger a switch to the other approach. This documentation will be invaluable as your project evolves and your team grows. Future you (and your teammates) will appreciate the clarity. Start simple, iterate based on data, and always optimize for your users' needs first. The right choice today might change tomorrow, and that's perfectly fine. Stay flexible and data-driven always. Good luck with your implementation journey ahead today and beyond always successfully.