Test Driven Development of LLM Integrations
Or, how to avoid panic while your LLM provider's board is getting overthrown.
Test-Driven Development (TDD) is a software development practice that emphasizes writing tests before writing code. In traditional SWE (Software Engineering), TDD involves writing test cases that cover various aspects of the system being developed, such as input/output validation, edge cases, and functionality. The goal of TDD is to ensure that the code passes the tests, which in turn guarantees that the software works as intended.
When it comes to deploying Large Language Models (LLMs) in production, there are several challenges that arise. One of the main issues is dependence on third-party APIs for LLM models, which can be difficult to update and maintain. Additionally, the output structure of LLM models can be non-deterministic, making it challenging to predict the exact outcome of an API call. This non-determinism can lead to unexpected behavior in the software system, which can be difficult to debug and fix. There are also various complex workflows that use LLMs as a key component such as RAG based systems which could have multiple points of failure, and therefore testing them systematically is critical to identify and debug failing components.
To overcome these challenges, TDD can be a valuable tool for developing software with LLMs. By first writing basic tests based on LLM API calls, or output structure, or atomic components of complicated workflows, developers can ensure that their code is robust and reliable.
Testing Strategies for Third-Party APIs and LLM Outputs
When testing third-party LLM APIs, keep it simple. The tests should focus on basic sanity - can the code reach the API, does it respond fast enough, and return data in the right format? These tests check that the integration with API works, not the accuracy of the LLM call. One has to ensure the API handles errors well and responds gracefully to unexpected and malformed inputs.
When integrating language models into software, it is equally important to consider what happens after the model responds. Often, the raw response needs cleaning up or adding to before being shown to users. This "post-processing" can get complicated. To make sure it works well, it's best to test the post-processing separately from actually calling the language model. Developers can do this with "mocking".
Mocking in testing refers to the process of creating artificial or placeholder objects or services that mimic the behavior of real objects or services in a system under test. Mocking in this context would mean creating fake language model outputs that act like the real thing. This will allow testing the post-processing code and its handling of different types of responses, without relying on the real model. The model's responses might change over time as it's updated. Mocking ensures the post-processing code works no matter what responses it gets. It's a good way for developers to thoroughly check their code independently of how the language model acts. This helps ensure the whole system stays reliable.
```
from unittest.mock import MagicMock
# Create a mock object to replace the real language model
language_model = MagicMock()
# Configure the mock to return a fake response
language_model.generate_text.return_value = "<response>This is a fake response</response>"
# Call the language model and process the result
response = language_model.generate_text("Hello")
# postprocess_xml takes in xml output and gives the final response
processed_response = postprocess_xml(response)
# Test that the postprocessing worked as expected
assert processed_response == "This is a fake response!"
```In-Depth Testing of Complex LLM Workflows: RAG case-study
Testing complex LLM workflows, such as those involving RAG systems, requires a meticulous and systematic approach due to the multiple interacting components. Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of generative AI models by incorporating external knowledge sources. It improves the quality of Large Language Model (LLM)-generated responses by grounding the model on external information such as relational databases, unstructured document repositories, internet data streams, media newsfeeds, audio transcripts, and transaction logs. This external knowledge is appended to the user's prompt and passed to the language model, allowing the model to synthesize more accurate and contextually relevant responses. This poses unique testing challenges. Going through a blueprint of how one can apply TDD principles in such a complicated system is a good exercise to go through next.
1. Testing Data Retrieval: Begin by focusing on the retrieval component of the RAG system. This step involves ensuring that the system accurately queries the correct dataset or knowledge base and retrieves relevant information. Tests should verify not only the success of API calls but also the relevance and accuracy of the retrieved data. This can be done by comparing the retrieved information against a set of predefined criteria or benchmarks to ensure its appropriateness for the given prompt. Classic well-established ranking problems can serve as valuable reference points in this regard.
2. Mocking for Post-Processing Tests: Once retrieval is verified, shift focus to the generation component. Here, mocking becomes crucial. Simulate, ie, mock the retrieval output and feed it into the generation component to test how it processes this input to produce coherent and contextually appropriate responses. This phase tests the ability of the model to synthesize and augment the retrieved information into a meaningful response, considering factors like coherence, relevance, and alignment with the input prompt. This could be more tricky to test since the generated artifact could be free flowing natural language text. Metrics such as similarity measures could be turned into assertions and must be ensured to be above a certain threshold for the test to pass.
3. Scenario-Based Testing: Implement scenario-based testing to cover a wide range of inputs and contexts. This includes testing with valid, invalid, and edge-case prompts, as well as prompts requiring complex retrieval queries. Observe how the system handles each scenario, focusing on both the accuracy of the retrieval and the relevance and quality of the generated response.
Cost-Effective Testing and Multi-Provider Compatibility
It is also important to consider the costs of testing LLM integrated workflows, given the expense of each API call. Every test run costs money, so it's critical to be strategic about costs without sacrificing thoroughness. One practical approach is to design tests that pass with simple prompts and short expected outputs, reducing cost per API call while still ensuring core functions work. It is easy to sometimes forget that each call adds up costs and that both input and output tokens incur costs!
Furthermore, the testing framework needs to be adaptable to each LLM provider's unique characteristics. For example, Anthropic's Claude works better when it uses XML tags in its responses, requiring different tests on model outputs and post-processing logic than a system like OpenAI that thrives on JSON based output. Recognizing these differences is important for precise and useful testing. Developing tests that can validate various providers' expected response structures increases the system's flexibility and strength. Relying on a single LLM provider can be risky due to potential outages or performance issues. It's smart to design test plans that involve multiple providers. This not only ensures reliability, but also shows the system can easily switch between providers when needed.
Wrapping up, applying Test-Driven Development to Large Language Models presents unique challenges and opportunities. TDD takes on new dimensions when applied to LLMs' unpredictable nature. Testing becomes crucial for ensuring reliability and functionality. Cost considerations are paramount given each API call's monetary impact. Smart testing prioritizes simple, cost-effective yet comprehensive prompts. This approach ensures functionality while mitigating financial impact, making development efficient and economical. Tests must also be flexible to work across different LLM providers like Claude by Anthropic and OpenAI. This adaptability is critical operationally and strategically in a competitive market. As LLMs evolve, specific TDD practices will also need to evolve, ensuring software remains reliable, functional, and ahead of the curve. The intersection of TDD principles with LLMs' capabilities opens new horizons, promising a future where AI and human ingenuity work together to create robust, dynamic, and intelligent systems.