The wave of artificial intelligence is crashing as people realize many large learning models just don’t live up to expectations. Models frequently return inaccurate information, outputs that resemble fever dreams, and questionable advice that makes old wives’ tales look like medical textbooks.
Engineers are aware of these drawbacks, and a new strategy for infusing models with specific contexts is gaining popularity in cutting-edge technological circles.
But what is that new strategy, and what makes it so effective?
It’s retrieval-augmented generation (RAG). RAG is an AI framework that pairs traditional database retrieval capabilities with those aforementioned large language models (LLMs). The duo gives more accurate responses thanks to contextual improvements and updated data sources.
At Netlify, we’re constantly pushing the envelope of what’s possible, including advances in AI, which is why we put together this guide on RAG AI. With Netlify and RAG AI, you can infuse your LLMs with improvements that make them more potent than emerging novelty.
In today’s article, we discuss RAG in-depth and analyze some potential AI applications, giving you the inspiration you need to explore this revolutionary advancement.
The architecture of RAG AI
Retrieval-augmented generation is an AI framework, meaning it’s the structure that makes it function. It’s the half of the equation that takes artificial intelligence from simple regurgitation to human-like complex reasoning.
To return these human-like results, RAG AI uses two stages: Retrieval and pre-processing, as well as content generation. During the retrieval and pre-processing stage, a user enters a prompt, triggering algorithms to scan external data for relevant information and perform a pre-processing step that includes tokenization, stemming, and removal of stop words.
The external information scanned by these algorithms could be web pages or knowledge bases. Vector databases are integral for the most effective models, as the mathematical representation of data makes it easier for machine learning (ML) and deep learning models to recall previous prompt engineering.
Vector databases also aid in embeddings, which are dense continuous vectors. These dense vectors represent coordinates in semantic space, helping models understand the relationships and contexts between words and enabling models to return answers that resemble human speech.
Once the scanning and pre-processing are complete, the next step kicks in—generation. During the generation stage, data from the pre-processing stage enters pre-trained LLMs to return a final output. The augmented nature allows LLMs to output results that make more contextual sense and are more factually accurate.
RAG AI in action: Real-world applications
DALL-E and OpenAI’s ChatGPT are public-centric services that garner the most attention, but RAG AI is quietly becoming the framework of choice for many industries. The move to RAG AI is based on improved accuracy and context support, which makes it feel less like you’re interacting with a machine.
As such, RAG AI has several emerging use cases that benefit from the optimization it brings. These sectors include:
- Customer service: Deploy more human-like AI chatbots on websites and support pages. These models help customers when you can’t have human representatives while maintaining a high degree of customer satisfaction.
- Content creation: Automate the creation of factually accurate content for blog posts, social media, or internal memos in a fraction of the time. RAG systems are less likely to return hallucinations and inaccurate data, a hallmark of previous generative AI models.
- Research: Compile and annotate stacks of resources without opening a book. AI research enables more complex research projects with natural language processing (NLP) that are capable of providing citations and relevant outputs to user queries.
- Healthcare: Provide better treatment to patients with AI models embedded with medical database repositories. WebMD’s belief that everyone is dying from rare diseases meets its match in RAG AI, which can return more realistic ailments based on patient symptoms.
One of the most significant benefits of retrieval-augmented generation is the improvements to the overall user experience. Chatbots have become more human-like in their generated responses, and more technical information enjoys updated databases, preventing the likelihood of inaccurate details.
RAG AI and the future of web development
AI is proving beneficial in web development and programming, as it can generate snippets of code that assist programmers in making their projects work. However, outdated training data and flawed or biased LLMs can create snippets that don’t work or are inefficient.
Enter RAG AI. Combining LLMs with up-to-date external knowledge sources can prevent flawed or biased training data from tainting your web development. The amount of accurate code you can generate dwarfs what a human can produce on their own.
Programmers also use RAG AI to update or generate release notes and documentation. Likewise, developers leverage the technology to generate user experience/user interface (UX/UI) ideas, pulling examples from external databases and creating mock-ups that personalize projects for demos, pitch decks, and project updates.
Building with RAG AI on Netlify
AI comes with some challenges, which we’ll cover a little later, but you can overcome many of those hurdles by using a platform that excels at handling next-gen AI projects. The Netlify platform supports composable RAG AI architecture, meaning if you have an idea, we have the tools to make it a reality.
Netlify is perfect for creating generative AI applications, leveraging LangChain, AI SDK, and LlamaIndex to support workflows. In fact, Netlify helps create solutions, such as:
AI chatbots to answer questions in real-time. Automate generation work, like images or content. Semantic searches of knowledge bases. Storing and processing new data to create embedding models and vector databases.
AI layers are also resource and server-dependent and rely on edge computing to perform rapid information retrieval. Netlify enables both serverless functions and edge computing, supporting the backend infrastructure so you can focus on the frontend experience.
Netlify complements vector databases by connecting to major providers, such as Pinecone, PlanetScale, Supabase, MongoDB, and Neo4J, to support the tools you need. Check out Netlify’s complete guide on building AI experiences that highlights specific use cases and code snippets.
RAG AI: Challenges and considerations
For all the advancements RAG brings, it is still an AI model and, therefore, comes with many of the same challenges and considerations of other LLM, ML, and algorithmic technologies. These challenges include:
- Limited iterative reasoning: RAG models struggle with complex, multi-step reasoning. The lack of iterative reasoning skills illustrates the gulf between human reasoning and the yes/no, on/off logic of computers, meaning results may lack the nuance of specific inquiries.
- Sensitivity to language: Human language is still able to “trick” RAG AI models, returning results that are not contextually appropriate, and while better than other models, RAG AI can still produce hallucinations.
- High computational costs: RAG AI is resource-hungry, requiring robust infrastructure to maintain rapid data retrieval. The high-speed processors, network components, and server requirements are costly to purchase and deploy. Their integration into current systems can also be complex, requiring professional fine-tuning.
- Data quality dependence: Like other frameworks, RAG AI relies on training data for the LLMs to function, meaning bad, biased, or outdated data tarnishes output results. Limited external resources can also further stifle creativity or create unhelpful echo chambers.
- Ethical concerns: RAG models raise ethical and legal concerns like any AI system: plagiarism, copyright laws, and intellectual originality. AI models pull data from existing work, often leading to plagiarism or stolen intellectual property.
Avoiding these pitfalls can be a full-time job, so it’s essential to prioritize the ethical deployment of RAG AI models. You should always use authorized, high-quality training data and datasets, give credit to original artists, and always have a human edit or verify results.
Build the future with Netlify
Retrieval-augmented generation transforms run-of-the-mill AI models into next-generation partners for professionals working in any industry. RAG AI introduces external resources, like vector databases, to return outputs that are more factually correct and contextually relevant.
While RAG AI can introduce some complexities, ultimately, it’s a robust framework for building purpose-built AI models. What better way to take advantage of the advanced technology than with a partner who supports that development?
Netlify’s platform is the perfect complement to RAG AI, and the tools found help developers create RAG models for any use case. Request a demo today and explore building RAG AI for yourself!
