A possibly apocryphal quote attributed to many leaders reads: “Amateurs talk strategy and tactics. Professionals talk operations.” Where the tactical perspective sees a thicket of sui generis problems, the operational perspective sees a pattern of organizational dysfunction to repair. Where the strategic perspective sees an opportunity, the operational perspective sees a challenge worth rising to.
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In part 1 of this essay, we introduced the tactical nuts and bolts of working with LLMs. In the next part, we will zoom out to cover the long-term strategic considerations. In this part, we discuss the operational aspects of building LLM applications that sit between strategy and tactics and bring rubber to meet roads.
Operating an LLM application raises some questions that are familiar from operating traditional software systems, often with a novel spin to keep things spicy. LLM applications also raise entirely new questions. We split these questions, and our answers, into four parts: data, models, product, and people.
For data, we answer: How and how often should you review LLM inputs and outputs? How do you measure and reduce test-prod skew?
For models, we answer: How do you integrate language models into the rest of the stack? How should you think about versioning models and migrating between models and versions?
For product, we answer: When should design be involved in the application development process, and why is it “as early as possible”? How do you design user experiences with rich human-in-the-loop feedback? How do you prioritize the many conflicting requirements? How do you calibrate product risk?
And finally, for people, we answer: Who should you hire to build a successful LLM application, and when should you hire them? How can you foster the right culture, one of experimentation? How should you use emerging LLM applications to build your own LLM application? Which is more critical: process or tooling?
As an AI language model, I do not have opinions and so cannot tell you whether the introduction you provided is “goated or nah.” However, I can say that the introduction properly sets the stage for the content that follows.
Operations: Developing and Managing LLM Applications and the Teams That Build Them
Data
Just as the quality of ingredients determines the dish’s taste, the quality of input data constrains the performance of machine learning systems. In addition, output data is the only way to tell whether the product is working or not. All the authors focus tightly on the data, looking at inputs and outputs for several hours a week to better understand the data distribution: its modes, its edge cases, and the limitations of models of it.
Check for development-prod skew
A common source of errors in traditional machine learning pipelines is train-serve skew. This happens when the data used in training differs from what the model encounters in production. Although we can use LLMs without training or fine-tuning, hence there’s no training set, a similar issue arises with development-prod data skew. Essentially, the data we test our systems on during development should mirror what the systems will face in production. If not, we might find our production accuracy suffering.
LLM development-prod skew can be categorized into two types: structural and content-based. Structural skew includes issues like formatting discrepancies, such as differences between a JSON dictionary with a list-type value and a JSON list, inconsistent casing, and errors like typos or sentence fragments. These errors can lead to unpredictable model performance because different LLMs are trained on specific data formats, and prompts can be highly sensitive to minor changes. Content-based or “semantic” skew refers to differences in the meaning or context of the data.
As in traditional ML, it’s useful to periodically measure skew between the LLM input/output pairs. Simple metrics like the length of inputs and outputs or specific formatting requirements (e.g., JSON or XML) are straightforward ways to track changes. For more “advanced” drift detection, consider clustering embeddings of input/output pairs to detect semantic drift, such as shifts in the topics users are discussing, which could indicate they are exploring areas the model hasn’t been exposed to before.
When testing changes, such as prompt engineering, ensure that holdout datasets are current and reflect the most recent types of user interactions. For example, if typos are common in production inputs, they should also be present in the holdout data. Beyond just numerical skew measurements, it’s beneficial to perform qualitative assessments on outputs. Regularly reviewing your model’s outputs—a practice colloquially known as “vibe checks”—ensures that the results align with expectations and remain relevant to user needs. Finally, incorporating nondeterminism into skew checks is also useful—by running the pipeline multiple times for each input in our testing dataset and analyzing all outputs, we increase the likelihood of catching anomalies that might occur only occasionally.
Look at samples of LLM inputs and outputs every day
LLMs are dynamic and constantly evolving. Despite their impressive zero-shot capabilities and often delightful outputs, their failure modes can be highly unpredictable. For custom tasks, regularly reviewing data samples is essential to developing an intuitive understanding of how LLMs perform.
Input-output pairs from production are the “real things, real places” (genchi genbutsu) of LLM applications, and they cannot be substituted. Recent research highlighted that developers’ perceptions of what constitutes “good” and “bad” outputs shift as they interact with more data (i.e., criteria drift). While developers can come up with some criteria upfront for evaluating LLM outputs, these predefined criteria are often incomplete. For instance, during the course of development, we might update the prompt to increase the probability of good responses and decrease the probability of bad ones. This iterative process of evaluation, reevaluation, and criteria update is necessary, as it’s difficult to predict either LLM behavior or human preference without directly observing the outputs.
To manage this effectively, we should log LLM inputs and outputs. By examining a sample of these logs daily, we can quickly identify and adapt to new patterns or failure modes. When we spot a new issue, we can immediately write an assertion or eval around it. Similarly, any updates to failure mode definitions should be reflected in the evaluation criteria. These “vibe checks” are signals of bad outputs; code and assertions operationalize them. Finally, this attitude must be socialized, for example by adding review or annotation of inputs and outputs to your on-call rotation.
Working with models
With LLM APIs, we can rely on intelligence from a handful of providers. While this is a boon, these dependencies also involve trade-offs on performance, latency, throughput, and cost. Also, as newer, better models drop (almost every month in the past year), we should be prepared to update our products as we deprecate old models and migrate to newer models. In this section, we share our lessons from working with technologies we don’t have full control over, where the models can’t be self-hosted and managed.
Generate structured output to ease downstream integration
For most real-world use cases, the output of an LLM will be consumed by a downstream application via some machine-readable format. For example, Rechat, a real-estate CRM, required structured responses for the frontend to render widgets. Similarly, Boba, a tool for generating product strategy ideas, needed structured output with fields for title, summary, plausibility score, and time horizon. Finally, LinkedIn shared about constraining the LLM to generate YAML, which is then used to decide which skill to use, as well as provide the parameters to invoke the skill.
This application pattern is an extreme version of Postel’s law: be liberal in what you accept (arbitrary natural language) and conservative in what you send (typed, machine-readable objects). As such, we expect it to be extremely durable.
Currently, Instructor and Outlines are the de facto standards for coaxing structured output from LLMs. If you’re using an LLM API (e.g., Anthropic, OpenAI), use Instructor; if you’re working with a self-hosted model (e.g., Hugging Face), use Outlines.
Migrating prompts across models is a pain in the ass
Sometimes, our carefully crafted prompts work superbly with one model but fall flat with another. This can happen when we’re switching between various model providers, as well as when we upgrade across versions of the same model.
For example, Voiceflow found that migrating from gpt-3.5-turbo-0301 to gpt-3.5-turbo-1106 led to a 10% drop on their intent classification task. (Thankfully, they had evals!) Similarly, GoDaddy observed a trend in the positive direction, where upgrading to version 1106 narrowed the performance gap between gpt-3.5-turbo and gpt-4. (Or, if you’re a glass-half-full person, you might be disappointed that gpt-4’s lead was reduced with the new upgrade)
Thus, if we have to migrate prompts across models, expect it to take more time than simply swapping the API endpoint. Don’t assume that plugging in the same prompt will lead to similar or better results. Also, having reliable, automated evals helps with measuring task performance before and after migration, and reduces the effort needed for manual verification.
Version and pin your models
In any machine learning pipeline, “changing anything changes everything“. This is particularly relevant as we rely on components like large language models (LLMs) that we don’t train ourselves and that can change without our knowledge.
Fortunately, many model providers offer the option to “pin” specific model versions (e.g., gpt-4-turbo-1106). This enables us to use a specific version of the model weights, ensuring they remain unchanged. Pinning model versions in production can help avoid unexpected changes in model behavior, which could lead to customer complaints about issues that may crop up when a model is swapped, such as overly verbose outputs or other unforeseen failure modes.
Additionally, consider maintaining a shadow pipeline that mirrors your production setup but uses the latest model versions. This enables safe experimentation and testing with new releases. Once you’ve validated the stability and quality of the outputs from these newer models, you can confidently update the model versions in your production environment.
Choose the smallest model that gets the job done
When working on a new application, it’s tempting to use the biggest, most powerful model available. But once we’ve established that the task is technically feasible, it’s worth experimenting if a smaller model can achieve comparable results.
The benefits of a smaller model are lower latency and cost. While it may be weaker, techniques like chain-of-thought, n-shot prompts, and in-context learning can help smaller models punch above their weight. Beyond LLM APIs, fine-tuning our specific tasks can also help increase performance.
Taken together, a carefully crafted workflow using a smaller model can often match, or even surpass, the output quality of a single large model, while being faster and cheaper. For example, this post shares anecdata of how Haiku + 10-shot prompt outperforms zero-shot Opus and GPT-4. In the long term, we expect to see more examples of flow-engineering with smaller models as the optimal balance of output quality, latency, and cost.
As another example, take the humble classification task. Lightweight models like DistilBERT (67M parameters) are a surprisingly strong baseline. The 400M parameter DistilBART is another great option—when fine-tuned on open source data, it could identify hallucinations with an ROC-AUC of 0.84, surpassing most LLMs at less than 5% of latency and cost.
The point is, don’t overlook smaller models. While it’s easy to throw a massive model at every problem, with some creativity and experimentation, we can often find a more efficient solution.
Product
While new technology offers new possibilities, the principles of building great products are timeless. Thus, even if we’re solving new problems for the first time, we don’t have to reinvent the wheel on product design. There’s a lot to gain from grounding our LLM application development in solid product fundamentals, allowing us to deliver real value to the people we serve.
Involve design early and often
Having a designer will push you to understand and think deeply about how your product can be built and presented to users. We sometimes stereotype designers as folks who take things and make them pretty. But beyond just the user interface, they also rethink how the user experience can be improved, even if it means breaking existing rules and paradigms.
Designers are especially gifted at reframing the user’s needs into various forms. Some of these forms are more tractable to solve than others, and thus, they may offer more or fewer opportunities for AI solutions. Like many other products, building AI products should be centered around the job to be done, not the technology that powers them.
Focus on asking yourself: “What job is the user asking this product to do for them? Is that job something a chatbot would be good at? How about autocomplete? Maybe something different!” Consider the existing design patterns and how they relate to the job-to-be-done. These are the invaluable assets that designers add to your team’s capabilities.
Design your UX for Human-in-the-Loop
One way to get quality annotations is to integrate Human-in-the-Loop (HITL) into the user experience (UX). By allowing users to provide feedback and corrections easily, we can improve the immediate output and collect valuable data to improve our models.
Imagine an e-commerce platform where users upload and categorize their products. There are several ways we could design the UX:
The user manually selects the right product category; an LLM periodically checks new products and corrects miscategorization on the backend.The user doesn’t select any category at all; an LLM periodically categorizes products on the backend (with potential errors).An LLM suggests a product category in real time, which the user can validate and update as needed.
While all three approaches involve an LLM, they provide very different UXes. The first approach puts the initial burden on the user and has the LLM acting as a postprocessing check. The second requires zero effort from the user but provides no transparency or control. The third strikes the right balance. By having the LLM suggest categories upfront, we reduce cognitive load on the user and they don’t have to learn our taxonomy to categorize their product! At the same time, by allowing the user to review and edit the suggestion, they have the final say in how their product is classified, putting control firmly in their hands. As a bonus, the third approach creates a natural feedback loop for model improvement. Suggestions that are good are accepted (positive labels) and those that are bad are updated (negative followed by positive labels).
This pattern of suggestion, user validation, and data collection is commonly seen in several applications:
Coding assistants: Where users can accept a suggestion (strong positive), accept and tweak a suggestion (positive), or ignore a suggestion (negative)Midjourney: Where users can choose to upscale and download the image (strong positive), vary an image (positive), or generate a new set of images (negative)Chatbots: Where users can provide thumbs ups (positive) or thumbs down (negative) on responses, or choose to regenerate a response if it was really bad (strong negative)
Feedback can be explicit or implicit. Explicit feedback is information users provide in response to a request by our product; implicit feedback is information we learn from user interactions without needing users to deliberately provide feedback. Coding assistants and Midjourney are examples of implicit feedback while thumbs up and thumb downs are explicit feedback. If we design our UX well, like coding assistants and Midjourney, we can collect plenty of implicit feedback to improve our product and models.
Prioritize your hierarchy of needs ruthlessly
As we think about putting our demo into production, we’ll have to think about the requirements for:
Reliability: 99.9% uptime, adherence to structured outputHarmlessness: Not generate offensive, NSFW, or otherwise harmful contentFactual consistency: Being faithful to the context provided, not making things upUsefulness: Relevant to the users’ needs and requestScalability: Latency SLAs, supported throughputCost: Because we don’t have unlimited budgetAnd more: Security, privacy, fairness, GDPR, DMA, etc.
If we try to tackle all these requirements at once, we’re never going to ship anything. Thus, we need to prioritize. Ruthlessly. This means being clear what is nonnegotiable (e.g., reliability, harmlessness) without which our product can’t function or won’t be viable. It’s all about identifying the minimum lovable product. We have to accept that the first version won’t be perfect, and just launch and iterate.
Calibrate your risk tolerance based on the use case
When deciding on the language model and level of scrutiny of an application, consider the use case and audience. For a customer-facing chatbot offering medical or financial advice, we’ll need a very high bar for safety and accuracy. Mistakes or bad output could cause real harm and erode trust. But for less critical applications, such as a recommender system, or internal-facing applications like content classification or summarization, excessively strict requirements only slow progress without adding much value.
This aligns with a recent a16z report showing that many companies are moving faster with internal LLM applications compared to external ones. By experimenting with AI for internal productivity, organizations can start capturing value while learning how to manage risk in a more controlled environment. Then, as they gain confidence, they can expand to customer-facing use cases.
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