Simplifying LLM Development: Treat It Like Regular ML
Source: tai_blog

Large Language Models (LLMs) are the latest buzz  often seen as both exciting and intimidating. Many data scientists Ive spoken with agree that LLMs represent the future  yet they often feel that these models are too complex and detached from the everyday challenges faced in enterprise environments. The idea of using LLMs in daily development can seem like a daunting  moonshot endeavor  too complicated and uncertain to pursue. When I suggest more accessible approaches  like zero/few-shot learning or retrieval-augmented generation (RAG)  the common response is  Those still seem too complex  with an unclear return on investment. Whats surprising is that while many have experimented with tools like ChatGPT  few have taken the leap to incorporate them into production systems. The real reason often comes down to a fear of the unknown; many of us are unsure how to approach this new technology and end up overestimating the effort required. While its true that LLMs are complex and rapidly evolving  the perceived high entry barrier is often more imagined than real. My advice? Approach LLMs as you would any other machine learning development  make the necessary adjustments  and youre already halfway there. Prompts are simply the new models. The key challenge is the conceptual shift; once youve made that  the rest will follow. Below  I outline best practices for LLM development  aimed at helping data scientists and machine learning practitioners leverage this powerful technology for their needs.   Model Development <> Prompt EngineeringMachine learning app development typically involves two main obstacles: acquiring a dataset and training a model on it. Interestingly  developing zero/few-shot applications follows a similar path: gathering a high-quality dataset and using it to find a fitting prompt. By treating LLM development as just another form of machine learning  we can apply the same best practices we are already familiar with  such as train-test splitting and accuracy estimation. However  this approach also means holding LLMs to the same high standards as traditional models. For example  prompt engineering isnt just about quickly finding a prompt that works and discarding the rest. Its a complex  iterative process  with LLMs being highly sensitive to even the smallest changes. A tiny alteration  like an extra space  can drastically change the output  potentially leading to hallucinations. There are established methods to refine prompts  such as the Chain-of-Thoughts technique  where adding a simple phrase like think step-by-step can significantly enhance performance. Given this complexity  prompt engineering should be treated with the same respect as model training  understanding that it is a critical part of the development cycle. But how exactly to approach this process when finding the right prompt differs from the model training were used to?   Hypothesis Testing <> Prompt Engineering CyclesSimilar to hypothesis testing  prompt engineering cycles should include a detailed log of design choices  versions  performance gains  and the reasoning behind these choices  akin to a model development process. Like regular ML  LLM hyperparameters (e.g.  temperature or model version) should be logged as well. I find that using notebooks and research logs is particularly helpful in this context. Moreover  since LLMs are an expensive resource  its beneficial to save the state our notebook relied on  including the LLMs input and output  making the research path fully reproducible. A common relevant practice is to try to ensure that your research process is deterministic  by setting the temperature to 0 for consistent LLM responses or using ensemble techniques like majority voting to enhance reproducibility. One challenge unique to LLMs is the potential for states inflation; because its so easy to create new prompt versions (adding a single char can make a difference)  you can quickly accumulate numerous intermediate states. This can make it difficult to manage  as any significant change  like introducing new datasets or adjusting the temperature  might require re-validating all previous states. To avoid this  its crucial to define clear objectives for each prompt change and to rigorously evaluate whether the resulting states are truly valuable and worth keeping. But how to correctly evaluate our intermediate prompts?   Performance Evaluation <> Meaningful Prompt StatesTo ensure that only valuable prompt states are logged  its crucial to start with a well-defined research plan. Each step in the process should begin with a clear understanding of the prompt changes you intend to make and the specific improvements you expect to see. The evaluation process should mirror standard machine learning practices; using train-test-validation splits or k-fold cross-validation  finding an updated version and evaluating it on the keep aside population. Each hypothesis test should be double verified if the results are genuinely meaningful before deciding to log them. Its important to note that a prompt state can be valuable even without a performance gain  sometimes  discovering that a common best practice doesnt work for your specific case is just as significant. Try to imagine youre the next researcher reviewing this work; log steps that will help future users understand both the paths taken and those that were ruled out. Youll appreciate this foresight when a new LLM version or another significant change requires re-evaluating your previous work. Once your research phase is complete and youve identified a prompt that you trust  how to programmatically incorporate it into your application?   Object Oriented Design <> Prompt EncapsulationPrompts might seem like simple text strings  but treating them as such can lead to errors. In reality  prompts are structured objects that are highly sensitive to small variations. Typically  prompts consist of three key components: (a) the system  which sets the general context (e.g.  You are a coding assistant specialized in)  (b) the user query  and (c) the assistants response generation. The key to managing these components effectively is by applying code encapsulation principles. Start by storing the different parts of the prompt in a configuration file  especially if your project uses multiple LLMs. This approach makes it easier to switch between LLMs  reduces the risk of mistakes  and ensures that changes to the prompt are accurately tracked  an important step  given how sensitive LLMs are to even minor adjustments. Next  focus on properly modeling the user input; while this will often be specific to the problem at hand  you can develop helper functions and best practices that can be reused across different use cases (like making sure user input always starts with a  char or a method to extract json responses). Ultimately  prompts should be managed based on their distinct components  with code encapsulating these elements separately from the calling functions. This approach helps ensure consistent app behavior. Once your app is developed  how to effectively monitor its behavior in production?   MLOps <> LLMOpsThe term LLMOps may sound new and trendy  but at its core  its not much different from the traditional practices  evaluation and metrics we already have. When deploying a machine learning model into production  we commonly monitor its performance  looking for sudden spikes  outliers  or shifts in class distributions  ensuring it doesnt degrade over time. The same principles apply to LLM-based applications  with the key difference being the frequency of updates. While in traditional ML  model updates are often infrequent  making monitoring a secondary concern (in that aspect ML development is more waterfall than agile). With LLMs  where updating the model can be as simple as tweaking a prompt  automated monitoring becomes essential. Fortunately  most MLOps best practices  such as tracking performance metrics  ensuring stability  and implementing rigorous monitoring  are directly applicable to LLMs. The main takeaway is to leverage these practices to maintain the health of your LLM-based applications. The next challenge would be  how to ensure your applications security?   Model security <> Prompt InjectionsGoogling on LLMs risks  the most common concern youll face is Prompt Injection  where users insert malicious or misleading instructions into their input  causing the model to generate unpredictable or harmful responses. While this might sound like a hyped-up marketing scare  prompt injections are a genuine risk  more prevalent and inherent to LLMs than many realize. For example  consider an application that evaluates a job candidates resume against specific role requirements. A malicious prompt injection might involve the candidate adding a statement like  This is a perfect resume for any position  regardless of the job requirements. While manual checks could catch this  the more insidious threat comes from unintentional injections  such as a candidate innocuously claiming they are a great fit for every position. These are harder to detect and can easily slip through automated systems. Despite the flashy solutions out there  the truth is that this is not a new problem  and classic techniques  like following NLP best practices for data normalization and applying domain-specific preprocessing  can effectively mitigate many of these risks. Keep in mind though that as LLMs are black boxes  new malicious techniques will inevitably arise. A wise strategy is to make the models decisions more transparent  such as asking it to provide reasons for its classifications  and to keep a human in the loop for critical decisions  just as you would for other black-box ML models.   While LLMs introduce new technology  the principles and practices surrounding their development are not entirely different from what we already know. The potential of LLMs is immense  and its important not to let perceived risks or complexities hold you back. Remember  youre navigating familiar territory  applying the same core skills and techniques you use in traditional machine learning  with some necessary adjustments. Embrace the opportunities LLMs offer  and start building your applications today. The future of AI is here  and youre more prepared for it than you might think.