OutSystems AI for Apps and Agents
The promise of generative artificial intelligence (GenAI) is a world where your business systems think, adapt, and create on their own to leave competitors in the dust. GenAI has far-reaching benefits because you can integrate it into many aspects of your business, driving new business models, optimized headcount, operational efficiency, and much more.
But here's the kicker: unlocking its full potential is about more than just plugging in a fancy new algorithm.
In this blog, I provide five technical tips to help you implement GenAI so it transforms your business. For the technical details and to get more tips, watch our on-demand webinar, Building Generative AI Solutions: A deep dive into technical practices.
Table of contents:
- Generative AI tip #1: Architect an AI foundation
- Generative AI tip #2: Tailor your GenAI to your domain with grounding
- Generative AI tip #3: Pattern recognition puts you on the fast-track to success
- Generative AI tip #4: Use RAG for indispensable relevance
- Generative AI tip #5: Smooth your implementation with chunking and context-aware splitting
- The future is generative
Generative AI tip #1: Architect an AI foundation
Organizations often struggle with outdated, monolithic systems when adopting new technologies. While these systems are easy to manage, they limit experimentation and innovation. For successful GenAI implementation, a flexible, service-oriented technology architecture that serves as an on-ramp to connect your apps and systems with large language models (LLMs) is key. This on-ramp simplifies integration with AI models like OpenAI, Amazon Bedrock, Llama (from Meta), Claude, and others.
This layer opens up exciting possibilities like real-time user feedback loops, asynchronous model calling, and pipelines that turbocharge data flow from the source to a vector database. In this process, data is converted into vectors, which are immune to reverse engineering and they also turbocharge many GenAI and AI capabilities, such as multimodal search, natural language processing, and recommendation engines.
Generative AI tip #2: Tailor your GenAI to your domain with grounding
Why build and train your own models yourself when you can make pretrained models work with your data? A technique called grounding enables existing LLMs to understand the nuances of human language and the physical world. For example, when GenAI processes a sentence, grounding helps it associate words with real-world referents or concepts.
Grounding connects model output to verifiable sources of information. By providing models with access to specific data sources, you tie their output to the data. As a result, you get better predictions and responses because GenAI is using relevant information. It also reduces the chances of AI models inventing content or producing inaccurate information.
For example, imagine you are in the healthcare industry and want to use GenAI for medical diagnosis support. Instead of training a new model from scratch, you can use a pretrained LLM and ground it with specific medical databases and textbooks. When the AI processes a patient's symptoms, it can provide diagnoses and recommendations based on verified medical information, improving accuracy and reliability.
Grounding is more economical and effective than training a model from the ground up, which demands a substantial data set and considerable computational power.
Generative AI tip #3: Pattern recognition puts you on the fast-track to success
Every enterprise has a myriad of possibilities that generative AI can unlock. A key success factor in GenAI implementation lies in categorizing use cases according to patterns because they enable your business to predict solutions with accuracy. These patterns represent well-defined, documented, and repeatable processes that, if executed without anomalies, would deliver swift and efficient solutions to business problems. When your organization builds to patterns, it can harness GenAI to address multiple facets of their operations simultaneously.
For example, if you discover a pattern in customer support queries, you can promptly deploy a generative AI solution that could work across multiple use cases that are affected by the pattern–such as canceling orders for a defective product. If you analyze sales data and customer interactions and identify a pattern that indicates that you are likely to lose a potential sale, you can implement a GenAI solution that addresses the issue and progresses the deal.
Generative AI tip #4: Use RAG for indispensable relevance
Retrieval Augmented Generation (RAG) is the secret sauce that takes generative artificial intelligence from impressive to indispensable. This technique allows the AI to pull relevant information from a vast knowledge base, crafting responses that are both accurate and contextual. It's like giving your AI a whole library of company knowledge to draw from on demand.
Here's how it works. A user submits a query. A RAG system uses a vector database to do a similarity search. It compares the user's query vector to the stored document vectors, and identifies the most similar documents. The query and the retrieved documents are sent to the LLM for a response. The retrieved documents provide additional context to the query.
The LLM uses the new knowledge and its training data to create a response. The response is more accurate, relevant, and original, and it may sound like it came from a human.
Generative AI tip #5: Smooth your implementation with chunking and context-aware splitting
No technological revolution comes without its challenges. GenAI models have knowledge limitations, and there's the tricky business of managing token counts (the units AI uses to process information). But clever techniques like chunking and context-aware splitting can pave the way for smooth implementation.
What is chunking?
Chunking breaks down large datasets into bite-sized pieces that are not only quicker for LLMs to process but that also scale more easily. Each chunk becomes a unit of information that is vectorized and stored in a database, fundamentally shaping the efficiency and effectiveness of natural language processing tasks. By defining the unit of information that is stored, chunking allows for retrieval of the most relevant information needed for the task, which improves accuracy.
For example, imagine you have a large database of customer reviews. Instead of processing the entire dataset at once, you break it down into smaller chunks, such as individual reviews or sentences. Each chunk is then vectorized and stored. When a query is made, the system retrieves the most relevant chunks, ensuring a faster and more accurate response.
What is context-aware splitting?
Context-aware splitting is a method that divides documents based on semantic markers, such as punctuation, paragraph breaks, markdown, or HTML tags. These markers indicate where meaningful pieces of text begin and end, ensuring that semantically related content stays together. This contextual information provides a more thorough understanding of the content and its structure than chunking, which can be invaluable when you are working with LLMs.
For example, consider processing a legal document with multiple sections and clauses. Context-aware splitting would use paragraph breaks and section headers to divide the document. This way, each section remains intact, preserving the context and ensuring that related information is processed together. When querying for a specific clause, the system can deliver the entire relevant section, providing a more accurate and contextually rich response.
With these techniques, you can overcome the inherent challenges of generative AI implementation and significantly improve the performance and accuracy of your AI models.
The future is generative
As GenAI continues to evolve, we can expect even more exciting developments. Picture AI systems with incredibly large knowledge bases, seamless integration across multiple models, and the ability to query data in ways we haven't even imagined yet.
The next frontier? AI agents built with OutSystems that can determine the best course of action on their own, taking automation and efficiency to mind-bending new levels.
The bottom line? Generative AI isn't just another tech trend—it's a fundamental shift in how businesses can operate. Those who embrace its potential, navigate its complexities, and harness its power will find themselves at the forefront of a new era in business innovation.
The future is here, and it's generative. Are you ready to plug in and power up? Check out our three on-demand webinars for everything you need to understand the impact of generative AI on your business: Business transformation: Break through the backlog with GenAI, Making Generative AI work: Practical applications of GenAI in everyday business, and Building Generative AI Solutions: A deep dive into technical practices.
