Evaluation of Large Language Model Applications
In this post, we provide developers with a practical guide for various evaluation phases - from initial model selection to continuous monitoring in production.
Introduction
The successful implementation of AI solutions goes beyond developing proof-of-concepts. A systematic evaluation is crucial for successful production operations and must accompany the entire lifecycle - from model selection to operational monitoring. A well-founded evaluation concept enables precise assessment of system performance in terms of accuracy, reliability, and consistency. This transparency is essential for targeted optimizations. The integration of evaluation processes across all development phases forms the foundation for continuous quality improvements, both during initial development and ongoing operations.
In this post, we provide developers with a practical guide for various evaluation phases - from initial model selection to continuous monitoring in production. You'll learn how to systematically assess and improve the quality, accuracy, and consistency of your language model-based solutions.
Evaluation Methods with System
The assessment of language models requires a multi-dimensional approach that combines different evaluation methods. Four central approaches have become established in practice:
- Standardized Benchmark Tests: Based on publicly accessible datasets, these enable direct comparison between different models and are particularly well-suited for initial model selection. However, these tests should only serve as rough orientation since they cannot adequately represent specific application requirements.
- Programmatic Evaluation: This involves automated procedures specifically tailored to the respective application. Developers can define their own metrics and implement task-specific evaluation criteria. This is particularly important for continuous quality assurance during the development process.
- Using One Language Model to Evaluate Another: This method enables differentiated analysis of outputs and can be particularly helpful in assessing aspects like coherence or logical consistency. However, it must be considered that the evaluating model may bring its own biases. Since language models tend to rate their own answers more positively, the evaluating model should differ from the model being evaluated.
- Manual Evaluation: Despite all automation, manual evaluation by human reviewers remains an indispensable component of quality assurance. Humans are particularly good at assessing subjective aspects such as comprehensibility, appropriateness, and cultural sensitivity. Even though this approach is time and resource-intensive, it provides important insights that automated procedures cannot capture.
Configuration Parameters and Evaluation Process
The configuration parameters and individual steps for evaluating Large Language Models (LLMs) are depicted in Figure 1 as a structured process. Developers first define the task areas to be tested - from general capabilities to summaries to specific classification tasks.
When configuring, teams can choose between two evaluation paths:
- The human path uses defined datasets and methods that are evaluated by selected teams.
- The automated path uses integrated or custom datasets and applies predefined algorithms.
The actual evaluation takes place along important dimensions such as accuracy, robustness, and factual knowledge. At the same time, the system analyzes possible stereotypes and toxic outputs. Teams can additionally define their own evaluation criteria.
Results are available in various report formats. While summaries provide a quick overview, detailed reports offer deeper insights into specific aspects. A comparison function helps in contrasting different models or configurations. All results are stored centrally in a datastore to enable long-term analyses and comparisons.
Core Principles of Successful Evaluation
Various central principles have crystallized for successful evaluation. First, assessments should be quickly executable - ideally in seconds rather than days. This allows for short development cycles and rapid feedback. Results must be quantifiable, meaning they should be based on measurable metrics rather than subjective assessments. Each evaluation should be traceable by transparently documenting the evaluation logic. The scope of test cases also plays an important role. Experience shows that often a hundred or more carefully selected test cases may be necessary for a well-founded evaluation.
Evaluation should be segmented, meaning that individual components of the system are evaluated separately. The test cases must cover various application scenarios to obtain a complete picture of system performance. Not least, proven traditional testing methods should also be incorporated into the evaluation.
Figure 2 shows how continuous validation of the various interaction and transition points between components can steadily improve the overall performance of the solution.
For example, in building a knowledge base using the Retrieval-Augmented Generation (RAG) pattern, the following interaction points can be derived:
- Generation of vector embeddings (numerical representations of words)
- Reordering of information (re-ranking)
- Information retrieval
- LLM prompt construction
- LLM response generation
Integration into CI/CD Pipeline
The seamless integration of evaluation processes into existing CI/CD pipelines is an essential aspect of modern AI development. This integration enables comprehensive integration tests and ensures that no regressions occur during the development of new features or updates.
A standard CI/CD pipeline for AI systems includes the following steps:
- Development: The source code repository stores the code and configuration of the AI system, including model instructions, system prompts, and tool configurations.
- Build: When changes occur, a build process is triggered that performs unit tests to validate the changes, including spell checks, syntax checks, and infrastructure and security checks.
- Integration: After deployment in the development environment, automated evaluations are performed to validate various components of the solution and conduct end-to-end tests.
- Pre-Prod: The solution can then be transferred to a pre-production environment where integration and performance tests, as well as business and user-oriented validations, take place. Here, AI-supported assessments can be used in combination with human review to evaluate model responses and reasoning chains.
- Production: In the final step, the solution is transferred to the production environment, where A/B tests, performance monitoring, and user analyses are conducted.
This structured approach ensures continuous quality assurance and allows development teams to identify and address problems early, before they can impact production operations.
Model Evaluation with Amazon Bedrock
Amazon Bedrock is a fully managed service, provides developers with straightforward access to Foundation Models (FMs) and their systematic evaluation. With just a few clicks, users can perform rapid assessments using predefined metrics. The evaluation parameters are flexible – developers can define their own data and metrics, and incorporate humans or other models into the testing cycle.
FIGURE 4: Results of a Model Evaluation using Amazon Bedrock
Amazon Bedrock provides a great starting point when beginning your journey implementing evaluations. However, as your solutions and agents become more complex, or when you need to integrate your telemetry into a broader Enterprise landscape utilizing LLLM frameworks such as AWS Strands, Langfuse and OpenTelemetry will provide greater flexibility. Which brings us to the our solution.
Evaluating Amazon Bedrock LLM solutions using AWS Strands and Langfuse.
Pre-requisites
Before running this code example, ensure you have:
- An IDE capable running Jupyter notebooks such as Visual Code Studio
- Python 3.10 or above
- And AWS account with AWS credentials setup in your environment
- Enabled access to the required models in your AWS Region
- Configured the appropriate IAM permissions for Amazon Bedrock and Amazon S3
- A Langfuse account
Overview
In this Example we will be evaluating an LLM solution for querying SEC 10-k filings. We will build an S3 Vector Knowledgebase, then upload and synchronize the 10-k documents into the Knowledge base so that they can be searched in a traditional RAG architectural pattern.
The ‘Financial Analysis Agent’ sends all its traces to Langfuse. The ‘Evaluation Agent’ pulls the traces from Langfuse and performs the evaluation of the Financial Analysis Agent and the Knowledge base using the trace files. The solution is evaluated against custom metrics we have created using the Ragas framework. The results of the evaluation are then sent these results back to Langfuse where they can be reviewed and analyzed.
Conclusion
The systematic evaluation of language model solutions is not a one-time event, but a continuous process that must be integrated into the entire development lifecycle. Only through consistent and comprehensive quality assurance can companies ensure that their AI solutions work reliably and effectively. The methods and tools presented in this post provide a solid foundation for practical implementation.
The importance of robust evaluation will continue to grow as more business-critical applications are based on language models. Companies that invest early in building systematic evaluation processes will have a decisive competitive advantage. The continuous development of evaluation methods and tools will help to steadily improve the quality and reliability of language model solutions.
Here are some next steps you can take to enhance your LLM evaluation processes:
- Implement Amazon SageMaker Clarify in your ML pipeline for enhanced model governance.
- Check out our Machine Learning Lens in the AWS Well-Architected Framework for best practices in building ML systems.
- Join the AWS Machine Learning community to connect with other developers and stay updated on the latest developments.
By leveraging these AWS services and best practices, you can ensure that your language model solutions are not only powerful but also reliable, fair, and compliant with your business requirements.
Resources
For more information and to dive deeper into the topics discussed in this post, check out these resources:
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