Large Language Models (LLMs) are incredibly powerful, but they are also incredibly fragile. Using LLMs in a production environment requires a lot of things to go right – effective prompting, safe implementation, and well-scoped outputs can make the difference between a stellar product implementation and a horrible user experience. Fortunately, LLMs have become easily accessible to the public through user-friendly interfaces like OpenAI’s Chat GPT-3 and GPT-4, making these powerful tools available for a wide range of users beyond just enterprises.
Unfortunately, many teams just don’t catch implementation issues until it’s too late. A prompt goes haywire, a hallucination slips through, or an output causes confusion and failure in a workflow, and suddenly, what was a valuable product differentiation collapses the user trust that you spent months building.
LLMs can streamline common tasks such as user authentication and pre-built workflows for onboarding and data ingestion, making everyday operations more efficient.
This is where LLM API mocking during testing can pay off big time. By using tools like Speedscale to capture, mock, and simulate LLM behaviour in your test suite, you can catch failures early before they’re exposed to your end users. The result? Fewer misfires, tighter control of your AI stack, and a better user experience – and improved trust – overall.
Introduction to Large Language Models
Large language models (LLMs) are a groundbreaking type of artificial intelligence (AI) designed to process and generate human language. These models are trained on vast amounts of data, including text from the internet, books, and other sources, to learn intricate patterns and relationships in language. By leveraging natural language processing (NLP) techniques, such as transformer architectures, LLMs can understand and generate text with remarkable accuracy.
The applications of LLMs are diverse and far-reaching. They can be used for language translation, text generation, and question answering, among other tasks. One of the key strengths of LLMs is their ability to be fine-tuned for specific tasks, such as generating code or creating content, through techniques like prompt engineering. This adaptability makes them a powerful tool for a wide range of AI applications.
LLMs are considered foundation models, meaning they serve as a robust starting point for various AI applications. They are typically based on deep learning techniques, such as neural networks, which enable them to learn complex patterns in language. Moreover, LLMs can generate text in multiple languages, including English, Spanish, French, and many others, making them versatile tools for global communication.
The potential of LLMs to revolutionize industries like customer service, content creation, and education is immense. However, their deployment also raises concerns about bias, accuracy, and security. These issues must be addressed through careful training and rigorous testing to ensure that LLMs are reliable and trustworthy.
Why Trust Is Everything in the Large Language Models Space
Traditional software can break in pretty obvious ways – error codes, log issues, crashed sessions, and all of these are reflected in a very clear way that users can navigate.
Conversely, LLMs don’t always fail obviously – they might generate answers that sound right but aren’t. They might tell you that they’re processing something when the underlying engine is hung. They might veer into inappropriate territory when prompt guardrails fail. Worst of all, they might do all of this – sometimes at the same time – while sounding authoritative and confident.
Unfortunately, this is a perfect recipe for user distrust.
That’s a recipe for user distrust. The ability for an API to offer authoritative solutions, engage in code generation, or handle complex tasks and challenges – all while potentially doing it wrong – can lead to a situation where a user is left in the dark.
Once a user sees an LLM hallucinate or provide a broken experience, their confidence takes a hit. And unlike traditional bugs, LLM misfires often feel personal – if your product uses a model to explain legal policies, recommend actions, or summarize sensitive information, a single wrong output can permanently erode user confidence.
Ultimately, trust is your most valuable resource in an LLM-enabled product – and burning it due to a bad prompt or poorly optimised workflow is a huge loss that might not be easy to reverse. However, integrating LLMs to provide context-aware responses that resemble conversations with human agents can enhance interactions and build trust. By augmenting traditional human roles with AI capabilities, businesses can improve customer experiences and streamline various processes.
Understanding APIs
An Application Programming Interface (API) is a set of definitions and protocols that allows different software systems to communicate with each other. APIs enable data exchange between systems, allowing them to request services or information from one another seamlessly. There are various types of APIs, including Simple Object Access Protocol (SOAP) APIs, Representational State Transfer (REST) APIs, and Web APIs, each with its own set of standards and use cases.
APIs play a crucial role in integrating different systems, such as web applications, mobile apps, and microservices. They provide a way for systems to communicate, enabling features like single sign-on, data sharing, and remote procedure calls. Depending on their intended use and accessibility, APIs can be public, private, or partner APIs. Public APIs are open to anyone, while private APIs are restricted to internal use within an organization. Partner APIs facilitate business-to-business partnerships and are typically restricted to authorized external developers.
The benefits of APIs are manifold. They can create new revenue streams, improve customer experiences, and increase operational efficiency. However, managing APIs requires careful attention to security, monitoring, and maintenance to ensure they function correctly and securely. This includes implementing authentication and authorization mechanisms, such as API keys, tokens, and encryption, to protect sensitive data and maintain system integrity.
Where Prompt Engineering Failures Happen
Prompt engineering is half science, half art – and often misunderstood. Even teams that are thoughtful about prompt structure and model selection can fall victim to implementation bugs like:
- Prompt injection due to uncontrolled user input
- Formatting errors that subtly change a prompt’s intent
- Overly verbose or ambiguous responses
- Unexpected model behaviors under load or in edge cases
The problem is that these sorts of issues aren’t always caught with traditional QA processes, especially when LLM services are block-box services or tooling accessed via external APIs like OpenAI or Anthropic. Making an API call often involves the use of authentication tokens and API keys. Authentication tokens authorize users and ensure they have the necessary access rights, while API keys verify the applications making the calls and allow for monitoring of API usage.
In many cases, even when traditional QA processes might catch the problem, a lack of other symptoms, for instance, no failure to function on complex requests and no overall lowered performance, might make it harder to detect issues that are not readily apparent.
Prompt failures can also worryingly have symptoms that feel like regular functionality. LLMs aren’t necessarily always cost-effective, and so failures of this type might just seem par for the course. A local machine might seem to be responding with accurate information without revealing that it lost connection to databases and systems some time ago. Mock data might be out of date or inaccurate, but the answers given could hide that fault quite easily.
Easy access to multiple data sets is crucial for developers in building LLM applications, as it allows for better data provisioning and cost management.
A Practical Example
Let’s look at a practical example. Let’s say you have a service that utilises an LLM to connect users to multiple data sources depending on their request. When a request is made through a frontend, this is processed as a prompt through an LLM to determine the proper data source. Additionally, LLMs are capable of translating text from one language to another, showcasing their versatility in handling various language tasks.
In this case, caching and LLM freshness become a significant issue. It’s entirely possible that the user request may be asking for the most recent data, and that the data captured by the internal scripts or interactions driven by the LLM may be out of date. This out-of-date variation may not actually be surfaced by the system or documented in a clear way (such as an endpoint without documentation), and as such, the system may serve data as fresh when it is actually not.
Cloud infrastructure plays a crucial role in facilitating the development of LLM applications by providing the necessary resources for managing data and experimenting with LLMs.
In some cases, this might be fine, but in many cases, it’s not. A service calling such fresh data might need it to locate a user order, determine the status of an order fulfillment, or something similarly time-bound and specific. In such cases, fresh data is paramount, but clarity on the freshness of the data is equally so – the last thing a service wants or a user needs is data marked as fresh that is useless and stale.
While some of this can be resolved with proper error logging and an understanding of data versioning, the ultimate problem is that the service, as built, is built within the confines of the LLM – and as such, the initial build stages are introducing complexities and issues that aren’t being resolved before production deployment.
Mocking LLM APIs with Speedscale
With this in mind, how should a software development approach resolve this issue? What is missing in this process is an effective mocking stage that identifies these potential pitfalls and implements a methodology to correct them. What we are missing is a mock API and LLM system which we can use to test mock responses, use scenarios, and the techniques and debugging steps that are being applied to the project, its file and library contents, and the ultimate response fed to the user. LLMs utilize neural networks for various tasks, making it crucial to simulate and test these interactions effectively.
Thankfully, Speedscale can help you achieve such a system with ease! Speedscale’s traffic replay and mocking capabilities are already popular for backend API testing. But when applied to LLM APIs, they become a powerful defense mechanism against prompt failures.
Machine learning models are integral to the development of LLMs, enhancing natural language understanding and processing through advanced algorithms and neural networks.
Using Speedscale, teams can:
- Replay traffic to simulate real-world user queries
- Mock LLM responses based on predefined expectations, detecting when output deviates from format or policy
- Test fallback logic when LLMs fail or return low-confidence outputs
- Benchmark latency and load behavior to identify operational risks
This lets teams treat their LLM usage like any other dependency, making the overall system observable, testable, and measurable. By building these tests into your CI pipeline, you can enforce prompt stability the same way you enforce schema correctness or authentication checks, ensuring quality and performance as designed.
Speedscale Scenarios for Testing LLM Integration
Let’s look at some scenarios where Speedscale can enhance development efficiency, unlocking parallel development and continuous delivery while boosting a developer’s awareness and code quality. Large language models (LLMs) are designed to interpret questions and generate responses, making them highly effective for various applications.
The importance of training data in the development and functioning of LLMs cannot be overstated. These models are trained on vast amounts of textual data to enhance their predictive capabilities and ensure accurate language generation.
Prompt Regression Tests via Traffic Replay
Speedscale can help you capture a set of common user inputs and run them through your prompt logic using mocked LLM responses. Using Speedscale’s toolset, you can:
- Replay actual user queries
- Validate that the structured output (e.g., JSON or markdown) adheres to format expectations, including the extraction and conversion of image text
- Alert if summaries exceed length, include disallowed language, or drop required context
For instance, examples of practical implementations include validating API integrations and ensuring that large language models (LLMs) handle diverse user inputs accurately.
Negative Testing for Edge Prompts
Speedscale isn’t just about validating what you already have – it can also help you test edge cases and scenarios that you create as representations of the “worst case scenario”, integrating edge case resolution effectively. For instance, you can inject malformed inputs to simulate edge behavior, such as:
- Incomplete sentences
- Mixed-language prompts
- Junk data or script tags
By testing these edge cases, you can ensure that your LLM implementation doesn’t return insecure or nonsensical content. Speedscale can validate the presence or absence of specific phrases in the response body, giving you quite flexible testing. Additionally, techniques like reinforcement learning are employed to enhance model performance for LLMs, ensuring they generate reliable and contextually appropriate outputs.
Reinforcement learning with human feedback (RLHF) is crucial in improving the performance of LLMs. This process helps mitigate issues such as biases and inaccuracies often present in generated content, making LLMs more reliable and suitable for enterprise use.
Fallback Scenario Simulation
One of the best benefits of using Speedscale is the fact that it grants you substantial control over the environment and network. Using Speedscale, you can mock upstream LLM timeouts or bad completions (e.g., blank or partially streamed output), confirming that virtual assistants and chatbots can still provide context-aware responses and maintain efficient customer service interactions. This ensures that:
- Your app returns a helpful fallback message
- No raw error messages leak to the user
- Fallbacks are logged for observability
Additionally, AI models play a crucial role in transforming business operations by enhancing efficiency and accountability through innovative governance practices.
Latency Testing Under Load
Traffic replay is the bread and butter of Speedscale’s solution. Using Speedscale, you can replay parallel LLM calls at high volume to observe system behavior. Transformer models, which serve as the foundational architecture for LLMs, are crucial in this context. This can help you understand and answer some fundamental questions, including:
- How does your orchestrator handle timeouts?
- Do you retry too aggressively?
- Does performance degrade silently?
Training LLMs on a vast amount of text is essential for their effectiveness in natural language processing tasks. Speedscale can generate LLM-like traffic at scale without burning real API tokens, allowing teams to test against quotas or SLAs without incurring cost or risk.
Security and Governance
Security is a critical aspect of large language models (LLMs) and APIs, as they can be vulnerable to attacks and data breaches. For LLMs, security can be enhanced through techniques like fine-tuning, which involves adjusting the model’s parameters to improve its performance on specific tasks. This process helps to mitigate risks and ensure that the model generates accurate and reliable outputs.
APIs can be secured through robust authentication and authorization mechanisms, such as API keys, tokens, and encryption. These measures help to protect data and ensure that only authorized users can access the API. Governance is equally important for LLMs and APIs, as it ensures they are used responsibly and in compliance with regulations. This involves establishing policies, procedures, and standards for the development, deployment, and maintenance of LLMs and APIs.
Effective governance includes ensuring that LLMs are trained on high-quality data, free from bias and inaccuracies, and that APIs are designed with security and scalability in mind. Regular monitoring and updates are essential to maintain the security and effectiveness of LLMs and APIs. This includes tracking their performance, identifying potential vulnerabilities, and addressing any issues that arise.
By prioritizing security and governance, organizations can ensure that their LLMs and APIs are reliable, trustworthy, and effective. This is critical for building trust with users, protecting sensitive data, and maintaining a competitive edge in the market.
Beyond the Mock: Building a Culture of LLM QA
As a final thought, teams should consider that this is just one step in the direction of a solid LLM plan. Mocking alone isn’t enough – teams need to build new forms of validation into the development lifecycle:
- Prompt reviews – just like code reviews, but for language clarity and safety
- Regression tests – for common inputs that should always produce safe, clear outputs
- Expectation contracts – defining what a model should return, and flagging when it doesn’t
Incorporating retrieval augmented generation (RAG) can significantly enhance content creation by leveraging advanced language model capabilities to generate refined and polished outputs. Additionally, modern web APIs, specifically REST APIs, play a crucial role in facilitating communication between web servers and browsers, representing a significant evolution in the API landscape.
Mocking makes these processes scalable. It provides the structure needed to support this new class of QA work – and ensures your LLM integrations don’t erode the very trust they’re designed to build. When paired with an excellent tool like Speedscale, this gets you going in the right direction and helps set the standard tone and approach for ensuring high quality. With high quality comes high trust, and when your users trust your system, their user experience improves by leaps and bounds.
Getting started with Speedscale is easy and free – you can sign up for a fully-featured free trial in mere minutes, and get started on your journey to mock LLM goodness!
