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Randall Hunt, Chief Technology Officer at Caylent, is a technology leader, investor, and hands-on-keyboard coder based in Los Angeles, CA. Previously, Randall led software and developer relations teams at Facebook, SpaceX, AWS, MongoDB, and NASA. Randall spends most of his time listening to customers, building demos, writing blog posts, and mentoring junior engineers. Python and C++ are his favorite programming languages, but he begrudgingly admits that Javascript rules the world. Outside of work, Randall loves to read science fiction, advise startups, travel, and ski.
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Guille Ojeda is a Senior Innovation Architect at Caylent, a speaker, author, and content creator. He has published 2 books, over 200 blog articles, and writes a free newsletter called Simple AWS with more than 45,000 subscribers. He's spoken at multiple AWS Summits and other events, and was recognized as AWS Builder of the Year in 2025.
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Deploying Large Language Models (LLM) into production without a robust evaluation framework is like deploying software without tests: simply a bad idea. Yet many organizations struggle with LLM evaluations because it's a bit different from traditional software testing paradigms.
Traditional software operates deterministically: given the same input, it produces the same output every time. In contrast, LLMs are non-deterministic. They generate outputs from a probability distribution across an effectively infinite space of possible responses. When we're expecting deterministic outputs, an LLM's probabilistic behavior might be a risk, but it'll still be tested in the same way as deterministic code. Testing becomes challenging when we're using the LLM's non-determinism as a feature, fully intending on generating different outputs from the same inputs. In traditional testing, we're used to a finite set of expected outputs, which we test for with a simple equality comparison: actual_output == expected_output. Now the set of acceptable outputs is effectively infinite. How do we test for that?
The difference isn't technical, and thus can't be solved with a few lines of code. We need to take a step back, and redefine how we decide whether an output is good or not. It's no longer sufficient for an output to be identical to a golden answer, quality now encompasses factual accuracy, contextual relevance, coherence across long interactions, safety from harmful content, and alignment with ethical norms.
Evaluation approaches can be organized along several key dimensions:
Programmatic evaluation methods are scalable, repeatable, and cost-effective, which means even if they don't cover everything you might want to evaluate, most of the time, it's a good idea to use them as either a starting point or a complement.
The earliest programmatic metrics operate on lexical overlap principles, quantifying similarity between model-generated text and human-written references by counting shared word sequences.
BLEU (Bilingual Evaluation Understudy), originally developed for machine translation, measures candidate text quality by calculating n-gram precision relative to reference translations. BLEU's fundamental limitation is its inability to handle lexical variation. It penalizes synonym use or alternative phrasing even when meaning is preserved, leading to poor correlation with human judgment for semantically diverse tasks.
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) takes a recall-oriented approach designed for summarization evaluation. It measures how many reference summary n-grams appear in the model-generated summary. ROUGE shares BLEU's core weakness: reliance on surface-level word overlap means it cannot capture semantic meaning and may reward summaries that are lexically similar but factually incorrect or incoherent.
METEOR (Metric for Evaluation of Translation with Explicit Ordering) performs word alignment between candidate and reference texts, giving credit not only for exact matches, but also for synonyms, stems, and paraphrases using resources like WordNet. METEOR calculates both precision and recall based on unigram matches, combines them via harmonic mean, and introduces a fragmentation penalty to reward fluency. While it improves correlation with human assessment, it still struggles with deep semantic and contextual nuances.
The primary problem of lexical metrics is their inability to understand that different words can convey identical meaning. This drove development of semantic evaluation methods leveraging pre-trained language models.
BERTScore pioneered this direction by using models like BERT to generate contextual embeddings for each token in candidate and reference texts. It computes cosine similarity between each candidate token and the most similar reference token, then aggregates these scores to calculate embedding-based precision (how well candidate tokens are supported by reference), recall (how well reference tokens are captured by candidate), and F1 score (the harmonic mean of precision and recall). This approach recognizes paraphrases and synonyms as high-quality matches, significantly improving correlation with human quality judgments.
As large language models have become more sophisticated, their applications have shifted toward open-ended, creative, and conversational tasks where single ground truth references often don't exist. This shift rendered traditional reference-based metrics inadequate, and while human evaluation is still an option, it isn't scalable. The field's response: using highly capable LLMs as automated, scalable proxies for human evaluators. This approach is called "LLM-as-a-Judge".
The core idea is leveraging state-of-the-art foundation models' contextual understanding capabilities to automate nuanced qualitative assessment. The main goal is to evaluate outputs where quality is subjective and multi-faceted, allowing us to automate the assessment of attributes like helpfulness, coherence, creativity, or persona adherence.
There are three primary ways to perform these assessments:
Pairwise Comparison presents the judge LLM with a prompt and two corresponding outputs (typically from different models, model versions, or different versions of your app). It determines which response is better according to given criteria, or declares a tie. This is considered more reliable than absolute scoring because it forces relative choice.
Direct Scoring evaluates a single output against a detailed rubric, instructing the model to assign scores on a Likert scale (commonly 1 to 5) for specific quality dimensions. For example, it might rate an answer's factuality, clarity, and conciseness separately. This provides more granular feedback than pairwise comparison but can be more susceptible to inconsistency.
Reference-free Evaluation assesses intrinsic text properties without needing golden answers. You can configure the judge to evaluate whether a chatbot response is polite, if an answer is factually consistent with provided source documents, or if generated code adheres to specific style guidelines.
The quality, fidelity and repeatability of your LLM-as-a-Judge evaluations depends significantly on the quality and precision of the evaluation prompt. Crafting effective judge prompts is a form of meta-prompt engineering aimed at eliciting consistent, unbiased, accurate judgments.
Our experiences at Caylent creating evaluations for multiple customers have led us to the following best practices:
Clear Criteria and Rubrics: Your prompt must provide explicit, unambiguous, well-defined rubrics. Vague instructions like "Is this a good answer?" produce unreliable results. Instead, you should break criteria down into specific, verifiable components. Don't just ask the LLM to "rate helpfulness," also define what helpfulness is, and give examples.
Chain-of-Thought (CoT) Prompting: Instructing the judge to "think step-by-step" before delivering verdicts improves reliability and transparency. This technique forces models to articulate reasoning, breaking complex judgments into simpler evaluation sequences. Additionally, you should instruct the Judge to output its rationale for the score.
Structured Output Formats: Instructing the judge to return assessments in structured formats like JSON or XML make it easy to parse scores, rationales and feedback, allowing programmatic analysis and aggregation across large datasets.
Example-based Calibration: The judge's alignment with human standards significantly improves if you provide few-shot examples of high-quality and low-quality evaluations within the prompt itself. Seeing concrete examples helps the model calibrate its internal standards and produce judgments more consistent with human expert assessments.
Here's an example of a Judge prompt:
The main advantage of LLM-as-a-Judge evaluations is scalability: you can evaluate thousands or millions of outputs at a fraction of the cost compared to using humans. This enables comprehensive testing and continuous monitoring that would otherwise be infeasible.
The main limitation is the difficulty in predicting the LLM's biases and how they'll affect its judgement. Known biases are salience (favoring longer and more verbose answers) and biases towards outputs from the same model family as the judge. Additionally, since the judge itself is a black box, its reasoning process can be opaque, undermining trust particularly in high-stakes scenarios where score justification matters as much as the score itself.
Moreover, this technique introduces a recursive dependency: the technology being evaluated now also serves as its own measurement instrument. This creates the potential for systemic echo chambers, where the AI development community inadvertently optimizes models and applications to please LLM judges rather than become genuinely more useful, creative, or correct for human end-users. If not carefully managed, models could converge on homogenous, verbose, superficially confident styles that score well in automated evaluations but lack true quality.
To mitigate bias risks and ensure LLM judges reflect desired human values, you need a rigorous calibration process that grounds automated evals in what you consider good answers.
You start by curating a small but diverse "golden" evaluation dataset, often comprising around 200 representative prompts covering common use cases, edge cases, and known failure modes. Then you (or domain experts) manually annotate this dataset, providing scores and detailed critiques for model outputs. This manual labeling phase forces you to solidify and articulate precise high-quality response criteria, allowing you to objectively assess whether the judge scores answers in the same way you would.
Once you establish ground truth, run the LLM judge on the same examples. Systematically compare its automated evaluations against human labels. Analyze discrepancies to identify error patterns in the judge's responses. Refine the evaluation prompt iteratively, clarifying criteria, adding examples, or adjusting output format. Keep refining until the agreement between yourself and the judge is above 85-90%. This ensures your scalable automated evaluation pipeline remains anchored to how you would evaluate answers.
Humans possess yet unparalleled abilities to understand context, appreciate nuance, detect subtle errors, and assess subjective qualities like creativity and tone. LLMs can do some of these things, but not at the same level as humans (at least not yet). You'll find that LLM-as-a-Judge is good enough for approximately 90% of your use cases, but if you want to go all the way to 100%, you'll need human involvement. This methodology goes beyond the initial vibe checks and the calibration of the judge, and involves humans in every execution of evaluations.
Human evaluation is the most reliable method for assessing inherently subjective qualities or those requiring deep contextual understanding. For creative writing, poetry generation, or marketing copy crafting, quality can only be judged by human aesthetic and creative sensibilities. Automated systems, even LLMs-as-a-Judge, struggle to detect subtle bias forms, cleverly disguised misinformation, or novel toxic content types. Human evaluators, especially those with domain expertise, can identify these nuanced failure modes and make better informed decisions than what state of the art LLMs can achieve.
Cost and scalability represent the most significant challenges for human evaluation. Properly using human evaluators requires assembling, training, and managing human evaluator teams. Moreover, execution of the evaluations takes significantly longer than automated evaluations.
Subjectivity and bias can emerge as different evaluators may interpret instructions differently or have varying quality standards, leading to low inter-annotator agreement. Furthermore, evaluators' cognitive biases, cultural backgrounds, and expertise levels can unconsciously influence ratings, introducing noise and potential unfairness. Factors like fatigue and attention drift can also make it difficult to ensure perfectly repeatable evaluations: the same evaluator might score identical outputs differently on separate occasions.
It's important to note that, despite all of these challenges, human evaluation is by far the most proven evaluation method. It's been used for centuries in schools and job interviews, and globalization has seen the need to standardize it across countries and cultures. It's terribly slow and expensive when compared to automated software tests (which is the comparison we started this article with), but it should be considered a viable option nonetheless.
The following table condenses our opinion on when to use each evaluation method. We hope this helps you make informed decisions based on your specific scenario.
| Evaluation Scenario | Programmatic Metrics | LLM-as-a-Judge | Human Evaluation | Recommended Approach |
|---|---|---|---|---|
| Development iteration (daily testing) | ✓ Primary | ✓ Secondary | ✗ | Programmatic first pass + selective judge for complex cases |
| Pre-release quality gate | ✓ Baseline check | ✓ Comprehensive | ✓ Critical/ambiguous cases | All three layers with human review of flagged outputs |
| Production monitoring (real-time) | ✓ Primary | ✓ Flagging system | ✓ Escalation queue | Automated detection with human review pipeline |
| Creative content evaluation | ✗ Insufficient | ✓ Primary | ✓ Calibration + sampling | Judge-based with human sampling for quality control |
| Safety-critical applications | ✓ First filter | ✓ Detailed assessment | ✓ Required validation | Human-primary hybrid with automated pre-screening |
| Cost optimization scenarios | ✓ Maximum coverage | ✓ Targeted use | ✓ Minimal (golden set only) | Heavily automated with strategic human checkpoints |
This table should guide your decisions regarding evaluation strategy, helping you balance speed, cost, and accuracy based on your specific use case requirements and risk tolerance.
Evaluating LLM-based agents — autonomous systems capable of reasoning, planning, and interacting with external tools and environments — needs yet another mindset shift. You'll need to go beyond static, single-turn text output assessment to dynamic, multi-step decision-making process analysis. It's not enough to evaluate each step individually, or the output as a whole. You'll need to evaluate each step with an understanding of the implications that different, seemingly interchangeable outputs will have in other steps down the line. It's a very interesting topic, but we'll have to leave it for a future article.
For organizations building on AWS infrastructure, Amazon Bedrock Evaluations is a good option. Amazon Bedrock provides a managed service for accessing foundation models, and its Evaluations feature provides evals capabilities. You can use Amazon Bedrock directly to generate the outputs and evaluate them, or you can Bring Your Own Inference, importing a JSONL file with your prompts and outputs, and letting Amazon Bedrock run automated metrics and LLM-as-a-Judge evaluations.
If you need to build more complex MLOps pipelines, you can use Amazon SageMaker Pipelines to create multi-step evaluation processes that run programmatic metrics, execute LLM-as-a-Judge evaluations, and aggregate results for comprehensive quality assessment. Amazon SageMaker's integration with the broader AWS ecosystem allows you to trigger evaluations automatically on model updates, schedule periodic quality checks, and maintain versioned evaluation datasets in Amazon S3.
Amazon CloudWatch allows you to create custom dashboards for quality tracking, and set up alarms that trigger when metrics fall below acceptable thresholds. Amazon CloudWatch Logs Insights enables you to query and analyze evaluation traces, helping you identify patterns in model failures or degradation over time.
The most effective approach to building reliable LLM applications is treating evaluation not as a final step but as a continuous, integrated part of your development lifecycle. Evaluations should be treated like software tests, and you should run evaluations wherever you would run software tests.
Taking this idea a step further and pulling a page from Test-Driven Development (TDD), you can take an "Eval-Driven Development" approach. The first step would be creating and versioning a Golden Dataset at the start of your project, containing representative prompt samples covering common use cases, challenging edge cases, and known failure modes, which would serve as your single source of truth for quality assessment. Next you would build automated, repeatable evaluation pipelines to run against your golden dataset after every significant application change (new models, new prompt versions, or updated retrieval strategies). On every pipeline execution you would monitor for regressions as your primary continuous evaluation goal, immediately detecting regressions (unintended performance degradations) by comparing evaluation scores of new versions against those of previous versions, which leads to quickly identifying whether changes improve performance of the application.
The following best practices synthesize key lessons that we at Caylent have learned from taking 200 Generative AI projects to production:
Before you start building automated evaluation systems, you should do a vibe check: Write a simple prompt or a simple app, and check the outputs yourself. This sounds counterintuitive, since it's obvious that it won't scale with time. However, our experience has shown us that this initial step gives you a good understanding of the model's capabilities and what to expect from your application, with minimal time investment. We've found this simple step leads to better results down the road, when you build your automated evaluations.
Create a carefully curated evaluation dataset that serves as your single source of truth. This dataset should include 200-500 representative examples covering common use cases, edge cases that stress model and application capabilities, and known failure modes. Ensure diversity across input types, complexity levels and expected output characteristics, and version control this dataset rigorously.
Implement a multi-tiered evaluation approach that balances speed, cost, and accuracy. Use programmatic metrics for immediate feedback, and add LLM-as-a-Judge evaluations for more nuanced quality assessments. Reserve human evaluation for high-stakes decisions, ambiguous cases, and calibration of automated systems.
For any LLM-as-a-Judge implementation, you must invest effort to properly calibrate them. Have domain experts manually evaluate a representative sample (typically 200 examples) from your golden dataset, and compare the outputs of the LLM judge against these expert judgments, analyzing discrepancies. Iteratively refine the judges’ prompts until you reach 85-90% agreement between the Judge and the experts. Recalibrate periodically as your application evolves.
Integrate evaluations into your CI/CD pipeline so that every significant code change, new prompt version, or model update triggers automated evaluation runs. Compare results against the baseline performance to immediately detect regressions. This prevents quality degradation from reaching production, and builds confidence in iterative improvements.
Evaluation doesn't end at deployment. Implement real-time monitoring of production outputs, tracking key quality metrics, user feedback signals, and system performance indicators. Set alert thresholds for critical metrics and establish processes for rapid responses and hotfixes when issues are detected. Make sure you implement proper observability to enable root cause analysis when problems occur.
Treat your evaluation system as critical infrastructure. Version all evaluation datasets, prompts, and rubrics. Document your evaluation methodologies and decision criteria. Control for randomness and version all dependencies, to ensure your evals are repeatable. Build institutional knowledge around evaluation practices to maintain consistency as your team evolves.
The final takeaway is that the LLM is part of your application, and evaluations are the only tests you can write for it. Treat evals like any type of software test, and don't deploy untested applications to production.
Building reliable LLMs doesn’t end with evaluation — it begins there. Robust testing, calibration, and monitoring are what transform experimentation into production-grade intelligence. At Caylent, our Generative AI practice helps organizations take that next step, evolving their GenAI strategy from idea to impact. From designing comprehensive LLMOps strategies to deploying Knowledge Bases and delivering tailored Generative AI strategies, our AWS-certified experts bring deep technical expertise and hands-on experience to help you accelerate innovation with confidence. Get in touch today to get started.
Randall Hunt
Guille Ojeda
Evaluate whether a customer service response provides actionable guidance.
CRITERION
Actionability: Does the response provide clear, specific steps the customer can immediately take?
- YES: Includes concrete actions with sufficient detail to execute
- NO: Remains vague, theoretical, or lacks practical guidance
EVALUATION PROCESS
Think step-by-step:
1. Identify what action the customer needs to take
2. Check if the response provides specific, executable steps
3. Note any vague language ("soon", "should", "might") that reduces actionability
4. Render your verdict with supporting evidence
OUTPUT FORMAT
{
"score": "YES" | "NO",
"reasoning": "Brief explanation with specific evidence",
"key_issue": "Main problem if score is NO, or main strength if YES"
}
CALIBRATION EXAMPLES
Example 1 - YES:
- Query: "How do I reset my password?"
- Response: "Click 'Forgot Password' on the login page, enter your email (john@example.com), then check your inbox for a reset link valid for 2 hours."
- Evaluation: `{"score": "YES", "reasoning": "Provides 3 specific steps with concrete details (button name, which email, time limit)", "key_issue": "Clear sequential actions"}`
Example 2 - NO:
- Query: "How do I reset my password?"
- Response: "You can reset your password through our account recovery process. Let me know if you need help!"
- Evaluation: `{"score": "NO", "reasoning": "Says what is possible but provides no steps on how to actually do it", "key_issue": "No executable instructions given"}`
---
NOW EVALUATE
Customer Query: {{CUSTOMER_MESSAGE}}
Response: {{RESPONSE}}
Your Evaluation:
