• Strategy: Effective Applications of Large Language Models in the Real World (And the Insights They Offer).

• Trends: 5 Steps to a Successful Gen AI Pilot.

• Tools: Poe - The Swiss Army Knife of AI Chat.

• Prompt: For a deep dive into the art of prompt engineering and to access specific examples, explore OpenAI's prompt engineering guide.

Strategy

Let's talk strategy in the midst of the AI revolution, where the pursuit of gold is not for the precious metal but for the transformative power of AI in business. The burning question, "Is there any substance behind the hype?" is astutely addressed in Forbes' latest piece, "Successful Real-World Use Cases For LLMs (And Lessons They Teach.)” This article skillfully illustrates the breadth of AI's influence across industries. Here’s a snapshot of the real-world examples highlighted:

Customer Service and Support:

• Claims Processing: An AI knowledge wiki for claims adjusters streamlines processing and enhances customer experience.

• Customer Service Questions: A semiconductor company improved customer service efficiency using LLMs to generate tailored responses.

• Immediate Customer Assistance: Vyopta enhanced customer support with an LLM, delivering prompt, context-specific assistance.

• Troubleshooting Equipment Issues: Ricoh accelerated technician training and troubleshooting with a specialized LLM.

Healthcare and Life Sciences:

• Clinical Diagnoses: OpenAI improved clinical diagnosis accuracy by 20% and reduced patient waiting times by customizing LLMs for healthcare.

• Curating Scientific Literature: A domain-specific LLM significantly reduced the time required for scientific literature analysis in pharmaceutical and life science sectors.

Business and Market Analysis:

• Customer Feedback Analysis: LLMs analyze product reviews to inform development and marketing strategies.

• Market Research: An LLM combined with other AI technologies revolutionizes market research and synthetic data generation.

• Analytics Review: Act-On used an LLM to allow users to interact with marketing campaign analytics in natural language.

Testing and Data Generation:

• Generating Test Data: LLMs facilitate the generation of realistic test data for application development.

As we navigate the future, the potential to leverage AI models and methods to amplify business results is boundless, limited only by our creativity. It is this very ingenuity that distinguishes us from Artificial Intelligence at this juncture. Our unique human capacity to pinpoint challenges and collectively mobilize an organization's strengths to harness AI for solutions is what truly sets us apart.

Trends

In previous editions of AI Quick Bytes, we've highlighted the crucial importance of defining clear goals, objectives, outcomes, speed, and trust for the success of AI implementation initiatives. Mathis Lucka of Deepset takes these elements a step further in his illuminating presentation, "5 Steps to a Successful Gen AI Pilot." He has a talent for demystifying the intricate, as you can see in the above featured snippet where he tackles the prioritization of AI Use Cases. You might wonder, what does strategy have to do with trends?

Excellent query!

Trend Prediction

Having an internal Data Science/Engineering team is a luxury not every enterprise can afford. Those with the resources for such teams often struggle to navigate the AI lifecycle from discovery and prototyping to production efficiently, if at all. For Startups to Mid-Sized companies, where AI is not a foundational expertise, the pressure is on to quickly gain proficiency and make a meaningful impact. I advocate for the adoption of a SaaS platform like Deepset, which simplifies the complexity of developing LLM-powered applications. A reusable, standardized toolkit is essential, as it saves teams from the redundant toil of crafting and upkeeping an LLM platform. Remember, model training is just one of the myriad challenges in deploying LLM-driven applications. This discovery and iterative process of finding the right fit of AI tools to output value is reminiscent of David Bland's "Testing Business Ideas" methodology, which we've enthusiastically endorsed in a prior newsletter.

Below are links to a videos that delves into each of the above steps. The victors in the race of AI innovation will be those who can swiftly and repeatedly iterate through this cycle, with the agility to deploy, monitor, and refine based on user feedback.

1. Find Use Case

2. Assemble Your Team

3. Map Use Case to Technology

4. Build a Prototype

5. Test ProtoType

Tools

Poe is the Swiss Army knife of AI models, giving you the flexibility to test drive a diverse set of chatbots to discover your perfect match. It offers access to a suite of AI models including well-known names like ChatGPT, DALL-E-3, Anthropic, Gemini, and Llama.

In my early trials with Poe, I'm seeing its potential as a valuable asset alongside my paid ChatGPT account. It’s my go-to for more straightforward prompts, leaving the heavy lifting to the bespoke bots I’ve honed for specific tasks. There is a monthly fee to access top tier LLM’s with message limits for the lower quality models, but a great way to subscribe to multiple Chatbots and see which one works best for you.

Which Model do you like best?

Prompts

Want to become a wizard in Prompt Engineering? Skip the wand and head straight to the source: the OpenAI Prompt Engineering Guide. It's your spellbook to conjure up some seriously potent prompts.

Try it out and share your favorite strategies / practices in the comments!