LLM-based applications, powered by Large Language Models (LLMs), are becoming increasingly popular. However, as these applications transition from prototypes to mature versions, it’s important to have a robust evaluation framework in place. This framework will ensure optimal performance and consistent results. Evaluating LLM-based applications involves collecting data, building a test set, and measuring performance using metrics such as factual consistency, semantic similarity, and latency. A comprehensive evaluation framework is crucial for the success of LLM-based applications.
Why evaluating LLM apps matters and how to get started
Introduction
Large Language Models (LLMs) are being incorporated into various applications, such as chatbots, assistants, and copilots. While LLMs offer rapid initial success, it is crucial to have a robust evaluation framework as you transition from a prototype to a mature LLM app. This blog post will cover:
– The difference between evaluating an LLM vs. an LLM-based application
– The importance of LLM app evaluation
– The challenges of LLM app evaluation
– Getting started with evaluation
Evaluating an LLM vs. an LLM-based application
Evaluating individual LLMs is typically done with benchmark tests. However, in this blog post, we focus on evaluating LLM-based applications. These applications are powered by an LLM and contain other components like an orchestration framework. An LLM-based application is built to execute specific tasks well. Evaluating an LLM-based application helps find the best setup for your use case.
Importance of LLM app evaluation
Setting up an evaluation system for your LLM-based application is important for three reasons:
1. Consistency: Ensure stable and reliable LLM app outputs and detect regressions. It is also important to assess how new versions of LLMs affect the performance of your app.
2. Insights: Understand where the LLM app performs well and identify areas for improvement.
3. Benchmarking: Establish performance standards, measure the effect of experiments, and confidently release new versions.
By achieving these outcomes, you gain user trust and satisfaction, increase stakeholder confidence, and boost your competitive advantage.
Challenges of LLM app evaluation
LLM app evaluation presents two main challenges:
1. Lack of labelled data: Unlike traditional machine learning applications, LLM-based apps don’t require labelled data to get started. This means there is no data to check how well the app is performing.
2. Multiple valid answers: LLM apps often have multiple correct answers for the same input. This makes evaluation more complex.
To address these challenges, you need to define appropriate data and metrics.
Getting started
To evaluate an LLM-based application, start by collecting data and building a test set. This test set consists of test cases with specific inputs and targets. Add examples on which the current model fails to iteratively build the test set. Involve business or end users to understand relevant test cases.
Measure evaluation performance by passing inputs to the LLM app and comparing the generated responses with the targets. Evaluate properties like factual consistency, pirateness, semantic similarity, verbosity, and latency. Depending on the use case, you can have more or different properties.
The LLM app evaluation framework
The evaluation framework involves passing test cases, properties, and the LLM app to an evaluator. The evaluator loops over the test cases, passes inputs to the LLM app, and evaluates the generated outputs based on properties. The evaluation results are stored for further analysis.
Collect user feedback and expand the test set to cover underrepresented cases. Use the evaluation results and feedback to improve the LLM app. Once you’re satisfied with the performance, release the new version of your application.
In conclusion, systematic evaluation is essential for LLM app development. It ensures consistent performance, provides insights for improvements, and drives the app’s success.
For more information on LLM-based applications, visit radix.ai or connect on LinkedIn. To explore AI solutions for your company, reach out to hello@itinai.com.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Steady the Course: Navigating the Evaluation of LLM-based Applications
- Towards Data Science – Medium
- Twitter – @itinaicom
