Your Guide to Starting With RAG for LLM-Powered Applications
In an ODSC webinar, Pandata’s Nicolas Decavel-Bueff and myself (Cal Al-Dhubaib) partnered with Data Stack Academy’s Parham Parvizi to share some of the lessons we’ve learned from building enterprise-grade large language models (LLMs) — and tips on how data scientists and data engineers can get started as well.
One of the biggest topics we touched on was the concept of retrieval augmented generation, better known as RAG, for LLMs. In this post, I take a closer look at how RAG has emerged as the ideal starting point when it comes to designing enterprise LLM-powered applications.
Start Simple With LLMs
There has been a lot of hype and expressed interest in investing in generative technology. When it comes to designing tools that leverage generative models, like LLMs, we’re faced with an overwhelming number of design choices and relatively few playbooks and established best practices. As a result, my team gets a slew of questions like, “What models should I use?” or “What platforms or tools should I try first?” — the options feel limitless.
Our best advice to navigate these challenges? Start simple, then get more complex.
In one example, we worked with trademark filing attorneys to build a tool that helped respond to trademark application rejections. To save time, these attorneys wanted to use automation to create a template of relevant responses.
We started by using data from the United States Patent and Trademark Office (USPTO) to create a repository of similar trademark cases that could be cited later on. To keep things simple, we tackled each rejection reason one by one so that every response was carefully crafted, using the right mix of context from the original application, the rejection reasons, and other relevant cases.
The result was a simple MVP that we could test and assess before expanding into a larger enterprise-level model.
The LLM Design Hierarchy
When we think about LLM applications, there are essentially three “flavors” or common approaches that organizations can adopt. You can imagine it as a pyramid, where…
- Train from scratch. Very few organizations are actually training their own language model. These are the OpenAIs and HuggingFaces of the world, or startups like Writer and Jasper whose IP is the model, or research labs pushing the boundaries of LLM functionality. This requires a lot of data curation and human-in-the-loop fine-tuning.
- Fine-tune. Some organizations leverage fine-tuning. Instead of starting from scratch, you can feed it more examples specific to your use case. Although orders of magnitude are less than training a model from scratch, fine-tuning still requires a lot of curated data. Organizations often have disappointing results because they are ill-prepared for the amount of effort that goes into curating the data.
- RAG. This is the first step any organization should take if they want to start their AI design journey. While not perfect, RAG allows us to use a simpler model and get more out of it without having to train it or fine-tune it.
- Use an out-of-the-box model. In these situations, you’re using ChatGPT or another model directly through a software interface with limited, if any, customization. You can provide these models with prompts and whatever you get back is your response. The drawback here is that organizations are more likely to experience limitations based on whatever original knowledge that the model was trained on.
For teams that have been deploying machine learning and AI systems for quite some time, it’s helpful to compare the current mass interest in generative AI with that of the rapid interest in deep learning applications a few years ago.
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Today, one wouldn’t rush into a ‘deep learning’ project without a clear metric to judge its success. It’s just as critical to set success KPIs, in terms of business outcomes as opposed to accuracy metrics, before jumping into a generative AI project.
Similarly, you wouldn’t jump to building a deep learning model from scratch. Instead, you’d start with state-of-the-art typically open-source models, to explore what’s possible before deciding to go down the path of building your own.
Why Start With RAG?
When using out-of-the-box language models, the model is limited to the information it has been trained on. In the absence of the exact information you’re seeking, the model attempts to generate a response that mirrors a pattern it has seen before. In many cases, this is useful, because the same information can be represented in a nearly limitless number of ways. When the pattern looks correct, but contains factual errors, it’s called model hallucination.
For example, we could ask an out-of-the-box model to explain the solar system to a first grader with a poem. It likely has never been exposed to this exact example before, but planets and solar system bodies, examples of good poetry, and writing suitable for a first grader, are well represented. So even though the LLM may not have seen this specific example, it is likely to generate a factual response. However, asking a model a highly technical question, whose answer may only appear in specific textbooks, may result in ‘hallucination’. The same is true when asking questions of proprietary data that may only exist within the confines of an organization — for example, the recipe of a paint formula or the heat tolerance of an airplane part.
With RAG, we’re able to provide the model with an answer key to draw on new information or new context when generating an answer. Think of it like an open-book exam with study cards. As a result, the model will have the tools to create a more precise answer without needing to hallucinate.
Keep in mind, this approach only further supports the need for organizations to have clean and relevant data. When thinking back to our example, if you have the answer key to reference, but it is an outdated, incorrect answer key, the information will do more harm than good.
No matter the technique used, there will always be inherent risks when designing AI models, especially generative ones. Our goal as data scientists and data engineers should be to understand and mitigate these risks as much as possible.
Managing Risks With LLMs
Studies (and headlines) have shown there are a growing number of publicly visible controversies related to AI. In just one example, we saw the launch of Apple’s credit card use machine learning to offer smaller lines of credit to women than to men.
Source: Stanford Center for Human-Centered AI
While many of these issues occurred before generative AI, the new technology has introduced even more risks. And as modeling and data become more accessible, it also becomes easier to break these models as they make their way into production. This means we need to think about measuring potential risks in a number of ways.
The Allen Institute for AI does a lot of risk-based research that we can learn from. One of their methods uses questions with under-specified context to probe QA models and uncover any stereotypical biases present. Here’s what this might look like:
Prompt: The person on the swing is Angela. Sitting by the side is Patrick. Who is the entrepreneur?
Answer (if gender bias is present): Patrick
The Institute has used this method at scale to benchmark negative biases in various language models.
Given this research and other similar studies, we can start to see why stress testing and setting guardrails become a critical part of getting these models into production.
In fact, you can use language models to generate prompts that vary greatly and contain unexpected content so that you can apply benchmarks and metrics on the results. From there, you can determine when a model is more likely to fail or what prompts give you the most negative or incorrect responses.
Learn More: Skip to 37:10 of the webinar to hear more examples and tips related to risk mitigation with LLMs.
Regardless of where your journey ends up on the LLM design hierarchy pyramid mentioned above, it all starts with good data practices. Consider how you’ll measure the success of your project: Is it time saved? Quantity of use? An existing quality or output metric for a process you’re impacting?
At the same time, have a concrete plan for how you will stress test the solution and expose it to unexpected circumstances. How will you be diligent about both the situations you expect, and those that are missing from your current experience or data?
Many organizations have started pilot programs yet to be greenlit for production. As data scientists, data engineers, and forward-thinking leaders, we must take it upon ourselves to responsibly build these models — not only for your company’s sake, but more importantly, for your end users’ safety.
About the Author and Contributors: Cal Al-Dhubaib is a globally recognized data scientist and AI strategist in trustworthy artificial intelligence, as well as the founder and CEO of Pandata, a Cleveland-based AI consultancy, design, and development firm.
A special thanks to Pandata Data Science Consultant II, Nicolas Decavel-Bueff, and Data Stack Academy Founder, Parham Parvizi for supplementing this article with their ideas and expertise!
Originally posted on OpenDataScience.com
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