Beyond Intuition: Building Principled LLM Applications

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Beyond Intuition: Building Principled LLM Applications

Large Language Models excel at intuition—the ability to understand and generate content without explicit reasoning. They're masters at "filling in the gaps," predicting the next token based on context with remarkable accuracy. This intuitive capability allows them to tackle complex problems and even ace challenging exams.

However, intuition alone has its limits, especially when dealing with projects that have multiple unique or conflicting constraints—scenarios that are "out of distribution" from typical training data.

The Problem: When Intuition Falls Short

Consider this example where we want to generate travel itineraries with specific requirements:

python
prompt = """
Generate interesting trip plans that are short, but also descriptive.
"""

The LLM might produce responses like this:

Portland Weekend Plan: "Explore indie bookstores with coffee in hand, then chase food carts and hidden speakeasies through the drizzle. End your night with vinyl playing in a candlelit bar as the city glows softly through misted windows."

While atmospheric, this example lacks the actionable details we need. It's vague about specific locations, timings, or concrete activities—failing to meet our "descriptive" constraint despite being appropriately "short."

The Solution: Principled Scoring Systems

To achieve consistent, high-quality outputs that adhere to all constraints, we need deterministic evaluation. This is where principled scoring comes in.

Using Pi's scoring system, we can create precise evaluation criteria:

python
from withpi import PiClient

# Initialize Pi client
pi = PiClient()

def score_trip_plan(llm_input, llm_output) -> float:
    scoring_spec = [
        {
            "label": "Engaging Content",
            "question": "Does the trip plan include engaging descriptions of destinations or activities?",
            "weight": 0.5
        },
        {
            "label": "Destination Highlights", 
            "question": "Does the trip plan highlight unique or interesting aspects of the destinations?",
            "weight": 0.5
        },
        {
            "label": "Actionable Details",
            "question": "Does the trip plan provide actionable details such as timings, locations, or costs?", 
            "weight": 0.5
        },
        {
            "label": "Activity Variety",
            "question": "Does the trip plan include a variety of activities to cater to different interests?",
            "weight": 0.3
        },
        {
            "label": "Cultural Insights", 
            "question": "Does the trip plan provide cultural insights or tips for the destinations?",
            "weight": 0.3
        },
        {
            "label": "Concise Length",
            "question": "Is the trip plan concise and free from unnecessary details?",
            "weight": 0.1
        }
    ]
    
    return pi.scoring_system.score(
        llm_input=llm_input, 
        llm_output=llm_output, 
        scoring_spec=scoring_spec
    ).total_score

Using Pi, we can generate a scorer for our use case. These questions will be evaluated quickly and accurately, using Pi's foundation model scorer.

Building the Principled Application

Now we can combine intuitive generation with principled evaluation:

python
def get_high_quality_trip_plan(prompt: str, quality_threshold: float = 0.7) -> str:
    """Generate trip plans until one meets our quality standards."""
    attempts = 0
    max_attempts = 10  # Prevent infinite loops
    
    while attempts < max_attempts:
        plan = generate_trip_plan(prompt)
        score = score_trip_plan(prompt, plan)
        
        if score >= quality_threshold:
            return plan
            
        attempts += 1
    
    # Fallback: return the best attempt if threshold isn't met
    return plan

The Power of Principled LLM Applications

This approach transforms unreliable "vibing" into systematic quality assurance. Instead of hoping the LLM gets it right, we:

Let the LLM brainstorm using its intuitive capabilities
Evaluate systematically against defined criteria
Iterate automatically until quality standards are met
Guarantee consistency across all outputs

Key Benefits

Reliability: Every output meets your defined quality standards
Scalability: Automated evaluation works at any scale
Flexibility: Easily adjust scoring criteria for different use cases
Transparency: Clear metrics show exactly why outputs succeed or fail

Beyond Trip Planning

This pattern applies to any LLM application with specific quality requirements:

Code generation with correctness and style constraints
Content creation balancing creativity with brand guidelines
Data analysis requiring both insight and accuracy
Customer support needing helpfulness and policy compliance

Conclusion

The future of LLM applications isn't just about better prompts—it's about combining the intuitive power of large models with principled evaluation systems. By building applications that generate iteratively and score deterministically, we can achieve the consistency and reliability that production systems demand.

When intuition meets principles, that's where the real magic happens.

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