5 Steps to Validate Your 1st Gen AI Use Case

I recently looked into various areas for introducing Generative AI use cases within any organization. If you look into your organization's ecosystem, all the tech folks are trying to find the use cases to implement Generative AI or how it can benefit them.

Therefore, what we are seeing as the approach is to identify and work on potential use cases positioned by the Product vendors who are primarily driving these use cases, while some organizations are actively trying to find a use case that solves a business problem with Gen AI rather than just jumping into use cases without any business objectives.

During a discussion with one of the organization's CIOs last week, he mentioned that he was looking to see if Gen AI could help his organization in two areas: reducing costs and assisting with speed to market.

This approach ensures due diligence before working on any Gen AI use case. This leads me to question how to validate a use case and determine if it fits the organization's goals.

I found an exciting webinar from Gartner called "How to Pilot Generative AI," which outlines five steps to follow for validating use cases for your organization.

To validate Generative AI (Large Language Models) use cases for implementation in any organization, here's a framework with specific tasks under each step:

1. Ideate:
   1. Task: Brainstorm potential use cases for Generative AI in your organization.
   2. Objective: Identify areas where Gen AI can add value, solve problems, or enhance processes.
2. Prioritize:
   1. Task: Assess and rank the brainstormed ideas based on feasibility, impact, and alignment with business goals.
   2. Objective: Focus on high-impact, viable projects that align with organizational priorities.
3. Build Team:
   1. Task: Assemble a team with the necessary Gen AI development and implementation skills. It may require additional hand-holding or skills development for the in-house teams.
   2. Objective: Ensure a blend of technical expertise (like data scientists and Gen AI specialists) and domain knowledge (business analysts, Subject matter experts, enterprise architects) in the team.
4. Design:
   1. Task: Develop an architecture of the chosen Gen AI application, including data requirements, model architecture, deployment strategy, and integration plans.
   2. Objective: To create a detailed plan for the LLM application that addresses specific organizational needs, ensuring seamless integration and user-friendly interaction. This design should cater to the nuances of LLMs, such as natural language understanding and generation capabilities, in the context of the targeted use case.
5. Iterate:
   1. Task: Develop the Gen AI-based solution in iterative cycles, regularly testing and refining to evaluate its language understanding and generation abilities. Check the model's performance in terms of accuracy, relevance of generated text, and response times.
   2. Objective: Continuously improve the use case for better alignment with the business and effectiveness. Collect feedback from both technical team members and end-users

Each step is crucial for a successful Generative AI implementation, ensuring that the chosen use case is technologically sound and strategically aligned with the organization's goals.

News & Updates...

This week's new AI features and products are announced, fueling the technology revolution.

1. The Open AI CEO was out and in, was with all of us for five days, and finally, the chaos was over with bringing him back to the company.
2. ​Claude 2.1 is now available over API in the console and powers claude.ai chat experience. Claude 2.1 delivers a 200K token context window, significant reductions in rates of model hallucination, system prompts, and a new beta feature that means you can upload entire codebases, financial statements, or long literary works for Claude to summarize, perform Q&A, forecast trends, compare and contrast multiple documents.
3. ​Stable Video Diffusion is the first foundation model for generative video based on the image model Stable Diffusion. The research paper is available here 'Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets'
4. ​ZipLoRA, is a method to cheaply and effectively merge independently trained style and subject LoRAs to generate any user-provided subject in any user-provided style.

The Cloud: the backbone of the AI revolution

• ​5 ways Generative AI Can Drive Value in Capital Markets, a blog from Google Cloud
• ​What is synthetic data? A blog published on Oracle Cloud
• ​The enterprise generative AI application lifecycle, a blog post published by Azure
• ​Build scalable and serverless RAG workflows with a vector engine, a post from the AWS cloud

Potential of AI

• How to Build a RAG-Powered Chatbot with Chat, Embed, and Rerank. An excellent article from Cohere​
• Three Ways Generative AI Can Bolster Cybersecurity, a report from Nvidia​

Things to Know

• ​RAGs is a Streamlit app that lets you create an RAG pipeline from a data source using natural language; it has three components. 1 - RAG Builder: Describe your RAG pipeline in natural language (data, parameters like top-k, system prompt), and let the builder agent build this for you. 2 - View Config: View the generated RAG configuration and make edits to it if you want to update the agent. 3 - RAG Agent: Ask the agent anything, and it'll answer the question.
• ​GAIA, is a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency.
• ​DiffSeg is an unsupervised zero-shot segmentation method using attention information from a stable-diffusion model. This repo implements the main DiffSeg algorithm and addtionally includes an experimental feature to add semantic labels to the masks based on a generated caption

The Opportunity...

Podcast:

• This week's Open Tech Talks episode 120 on A Techie's Guide to Life Beyond the Keyboard with Sylvia Worsham​
• ​

Courses to attend:

• Introduction to Large Language Models, YouTube video​
• Databricks: Large Language Models: Application through Production
• Free generative AI training for in-demand jobs.

Events:

• ​How to Build, Evaluate, and Iterate on LLM Agents, a FREE one-hour from DeepLearning.ai
• ​American Society for AI event on December 8 + 9, 2023, in San Francisco, California.
• ​Where AI meets the world, 28-29 FEBRUARY 2024, MARINA BAY SANDS, SINGAPORE
• ​7th Middle East Banking AI & Analytics Summit 2024, February 29th, 2024, Dubai.

Tech and Tools...

• ​Tuna is a no-code tool for quickly generating LLM fine-tuning datasets from scratch. This enables anyone to create high-quality training data for fine-tuning large language models like the LLaMas. There is both a web interface (Streamlit) and a Python script.
• ​Generative Models by Stability AI is an image-to-video model.
• ​

Until next week,

Kashif Manzoor

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The opinions expressed here are solely my own conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.