With each new update, generative AI advances by leaps and bounds, creating new opportunities for businesses of all kinds—if they can keep up.
The recently released GPT-4, for example, is 10x more advanced than the already sophisticated GPT-3.5, with better language and information synthesis abilities as well as greater accuracy. Meanwhile, more and more organizations—including the likes of Microsoft, Meta, and Google—are integrating generative AI applications into their own software.
Earlier in this article series, we reviewed best practices for getting the most out of generative AI, as well as ways to begin integrating these models into your workflow. But these just scratch the surface of generative AI’s power in the hands of advanced, tech-savvy users.
Here's how developers are learning to work with generative AI, along with some of the cutting-edge software applications using generative AI to revolutionize operations.
Generative AI application programming interfaces (APIs) can equip other software products with AI capabilities—marking a radical departure from how traditional coding works.
Typically, instructing a computer to perform a function requires a specific instruction for each individual task, which then creates the corresponding output. As anyone who’s taken a coding class will recognize, asking a computer to say “Hello, World” in the C++ coding language would look something like this:
<< "Hello, World!" << ;
The computer can’t creatively interpret the input in any way, so even the slightest ambiguity in the code will produce an error. It functions instead like a calculator, capable of generating the correct output when asked to add two numbers together, but unable go beyond a contained world of programmatic statements or defined values.
Generative AI models like GPT-4, on the other hand, work conversationally. While you could easily ask ChatGPT to say “Hello, World” in plain language, you could also instruct it to have an entire conversation with you beginning with that phrase.
Trained to detect patterns and relationships in mind-bogglingly large datasets, generative AI models can understand complex instructions, create outputs in the face of unstructured problems, and understand the tone and intent behind questions. It’s more like talking to a person, who rather than responding to the open-ended question of “why do humans like poetry” with “ERROR,” instead weighs possible answers and responds with a probable explanation.
The potential applications of this new interface are limitless. Beyond text and image generation, OpenAI’s example use cases demonstrate GPT-4’s wide range of capabilities. Here are a few key examples:
Because generative AI is still new, many developers are collaborating and experimenting with the best way to incorporate this technology into their products. Langchain, for example, is an open-source project focused on large language model (LLM) applications that assist with functions including:
Prompting: Prompt management, prompt optimization, generic interface for all LLMs, and common utilities
Chaining: Sequences of calls for integrations and end-to-end applications involving LLMs
Data-augmented generation: Pulling, analyzing, and relaying information from an external data source
Agents: Making decisions, taking action, and observing the outcome over an iterative process
Memory: Creating a persistent state between calls of a chain or agent
Evaluation: Using an LLM to evaluate its own performance
Similarly, services like Promptbase allow developers to buy and sell prompts for generative AI models that produce better results and save on costs, without needing to hire a dedicated prompt engineer. And as generative AI improves its factual accuracy and creativity capacity and expands into new mediums (e.g., real-time video overlay), it’ll become a better collaborator within product teams, capable of generating prompts and interfacing with other AI models to improve results.
In the meantime, just as generative AI learns by seeing what has come before, the best way to discover new applications and integrations for the technology is to experiment and explore. If you’re looking for ways to improve your products with AI, we’re here to help.
This is the third article in a three-part series on unlocking the power of generative AI.