Evals in AI Software Engineering: Optimizing Performance and Cost

Evals in AI Software Engineering: Optimizing Performance and Cost

In the rapidly evolving landscape of AI, the selection of the right language model for specific tasks has become a critical engineering decision. This choice impacts not only performance but also significantly affects operational costs. Enter "evals" - evaluation frameworks that serve as the compass guiding AI engineers through the maze of model options and configurations. Let's explore how evals are revolutionizing AI system design, particularly in creating cost-efficient, adaptive systems.

What Are Evals in AI Engineering?

Evals (evaluations) are systematic frameworks designed to assess the performance, capabilities, and limitations of large language models (LLMs) and LLM-powered systems. Unlike casual testing, evals provide structured, reproducible benchmarks that measure how well models perform across various dimensions relevant to your specific use cases.

According to OpenAI's evals framework documentation, evals offer "a framework for evaluating LLMs or systems built using LLMs" and include "an existing registry of evals to test different dimensions" of models [1]. These evaluation frameworks allow engineers to:

  • Compare different models objectively
  • Measure performance improvements over time
  • Identify strengths and weaknesses in specific capabilities
  • Create custom benchmarks aligned with business goals

The Business Case for Rigorous Evals

Why should engineering teams invest time in building robust evaluation frameworks? The answer lies in the substantial impact on both performance and cost.

A McKinsey study emphasized that "AI systems lacking transparent evaluation frameworks often fail to achieve anticipated productivity gains" [2]. Without proper evaluation, organizations risk:

  1. Overspending on unnecessarily powerful models
  2. Deploying models that underperform on critical tasks
  3. Missing opportunities to optimize for specific use cases

The "Right-Sizing" Approach: Using Evals for Model Selection

One of the most powerful applications of evals is determining precisely which model is appropriate for each task or subset of tasks. This approach rejects the "one-size-fits-all" mentality in favor of intelligent matching between task complexity and model capability.

The principle is straightforward: If a cheaper, smaller model passes your carefully designed evals for specific tasks, use it instead of defaulting to the most powerful (and expensive) option.

For example, a simple query about business hours might be handled perfectly well by a smaller, cheaper model, while a complex request for creative content development might require a more sophisticated model. Proper evals help quantify these distinctions objectively.

Model Cascades: The Intelligent Escalation Approach

Taking the right-sizing concept further, innovative AI engineers are now implementing "model cascades" - tiered architectures where queries are dynamically routed between models of different capabilities and costs based on task complexity.

How Model Cascades Work

A model cascade typically follows this process:

  1. Initial Assessment: An incoming query is first analyzed for complexity
  2. Basic Handling: Simple queries are routed to smaller, cheaper models
  3. Evaluation: The system evaluates whether the response meets quality thresholds
  4. Escalation When Needed: If quality thresholds aren't met, the query is escalated to more powerful models
  5. Continuous Learning: The system learns which types of queries are best handled by which models

This approach has shown impressive results. According to one case study, implementing a cascade architecture resulted in approximately 30% cost savings without sacrificing quality [3]. Another research paper noted that "building LLM cascades for saving the cost of few-shot LLMs in reasoning tasks" demonstrated significant efficiency improvements [4].

Self-Evaluating AI Systems

Perhaps the most fascinating evolution in eval implementation is the development of self-evaluating systems, where LLMs themselves participate in the evaluation process.

In this approach:

  1. A model generates a response to a query
  2. The same or different model evaluates the quality of that response based on predefined criteria
  3. If the response doesn't meet quality thresholds, the system escalates to a more capable model

This "LLM-as-a-judge" approach provides "human-like evaluation quality with up to 98% cost savings" compared to manual evaluation processes [5]. What's more, this approach "dramatically reduces evaluation time from weeks to hours while maintaining consistent evaluation standards across large datasets."

Implementing Evals in Your AI Engineering Practice

Ready to incorporate evals into your AI engineering workflow? Here's a practical approach:

1. Define Evaluation Criteria

Start by identifying the specific dimensions on which your models should be evaluated:

  • Task accuracy
  • Response relevance
  • Hallucination rate
  • Factual correctness
  • Latency and performance
  • Cost efficiency

2. Create Representative Test Sets

Develop test sets that accurately represent your actual use cases:

  • Include edge cases and challenging examples
  • Cover the full range of expected inputs
  • Incorporate user-specific terminology or domain knowledge

3. Establish Quantitative Metrics

Define clear, measurable metrics for each evaluation dimension:

  • Success rate on task completion
  • Similarity scores for relevance
  • Error rates for factual accuracy
  • Response time measurements
  • Cost per query calculations

4. Build Automated Evaluation Pipelines

Implement continuous evaluation throughout your model lifecycle:

  • Evaluate during development
  • Perform acceptance testing before deployment
  • Monitor performance in production
  • Re-evaluate when models or prompts change

Case Study: Gemini's Document Handling Cascade

An excellent example of cascade architecture in action is Google's approach to document handling in Gemini. When processing PDFs, Gemini implements an intelligent cascade that routes document analysis tasks based on complexity.

Gemini's system can:

  • Use simpler models for basic text extraction and summarization
  • Escalate to more sophisticated models for complex document understanding
  • Adapt to different document types and structures dynamically
  • Optimize for both performance and cost-efficiency

This approach enables Gemini to "save you hours of tedious PDF analysis" while maintaining high quality across different document types [6]. The free version handles basic document types efficiently, while the paid version supports more complex file formats - demonstrating how cascading approaches can be used to create tiered service offerings.

Building Your Own Model Cascade

If you're interested in implementing a model cascade in your own AI systems, consider this basic architecture:

  1. Router Component: Analyzes incoming queries and makes initial routing decisions
  2. Model Tiers: Define multiple tiers of models with different capabilities and costs
  3. Quality Evaluators: Implement criteria to assess response quality at each tier
  4. Escalation Logic: Create rules for when to escalate to more powerful models
  5. Feedback Loop: Continuously improve routing decisions based on results

The implementation can be as simple as:

def process_query(query, context=None):
    # Assess complexity
    complexity_score = assess_complexity(query)

    # Start with the cheapest model
    current_model = get_appropriate_model(complexity_score)

    # Generate response
    response = current_model.generate(query, context)

    # Evaluate quality
    quality_score = evaluate_response(query, response)

    # Escalate if needed
    if quality_score < QUALITY_THRESHOLD:
        # Try with a more powerful model
        advanced_model = get_next_tier_model(current_model)
        response = advanced_model.generate(query, context)

    return response

Conclusion: The Future of AI Engineering is Eval-Driven

As AI systems become more complex and widespread, the role of evals in AI engineering will only grow in importance. Organizations that invest in robust evaluation frameworks and implement intelligent model selection strategies will gain competitive advantages through:

  • More cost-efficient AI operations
  • Higher quality responses to user queries
  • Greater adaptability to diverse use cases
  • Improved ability to benchmark and measure improvement

By embracing evals as a core part of your AI engineering practice, you position your team to make data-driven decisions about model selection and deployment, ensuring that you're always using the right tool for the job - no more, no less.

References

[1] OpenAI Evals: A framework for evaluating LLMs and LLM systems

[2] A Complete Guide to LLM Evaluation For Enterprise AI Success

[3] How We Reduced Our LLM Inference Costs by 70% Without Sacrificing Quality

[4] Large Language Model Cascades with Mixture of Thought

[5] LLM-as-a-judge on Amazon Bedrock Model Evaluation

[6] Gemini's new free feature can save you hours of tedious PDF analysis