HCLTech's Rapid Azure Gen AI Experimentation service enables businesses to pilot LLM's based use cases, accelerating their AI innovation journey.
HCLTech’s Rapid Azure Gen AI Experimentation offering allows customers to evaluate various LLM's by embracing product design, sprint, and lean start-up principles to rapidly experiment and validate ideas, Powering their Innovation Journey.
Below is the 'Approach' for LLM experimentations. All/some of these processes can be customized and implemented for the experimentation depending on the complexity of the use case, the business expectations, and the timescale.
• Set Objectives: Define the business objectives and goals for Azure Gen AI adoption
• Identify Use cases: Assess current systems and operations to identify specific use cases where AI can be implemented
• Develop Proof of Concept: Select a high-impact use case and develop a PoC to test the feasibility and effectiveness of the chosen AI technology
• Establish a Data Strategy: Develop a comprehensive data strategy to ensure the availability of high-quality, clean, and secure data for AI systems
• Build a Skilled Team: Assemble a team of AI experts, data scientists, and domain experts to develop, implement, and maintain AI solutions
• Uptrain/Fine-Tune LLM Model: Uptrain the LLM using labeled domain data and Prompt engineering to fine-tune output
• Integrate AI Systems: Integrate AI existing systems and processes to Ensure seamless integration with other enterprise systems
• Monitor and Optimize: Track key performance metrics and evaluate the impact of AI on operations, customer experience, and financial outcomes
Business Framing (Week 1-2):
• Problem Statement Definition • Business Case Creation • Perceived value estimate • Analyze and Understand current platform capabilities • Evaluate data availability or cost to procure data • Cost & timeline estimate for experimentation
Solution Design & Set-up (Week 3-4)
• Develop solution design and design alternatives • Enable GenAI platform, ensuring proper integrations and workflows
Deliver Proof Of Concept (Week 5-10)
• Define success parameters • Acquire data • Rapid Exploratory Analysis & Model Building • Prompt engineering & Retrieval augmentation • Validation & Evaluation
POC Outcome (Week 11-12)
• Successful experimentation with LLM models to demonstrate the feasibility of using generative AI • Business Case Validation • Well-defined AI performance metrics • Roadmap for implementing production environment
• Feasibility report with summarized findings from the POC/ experiment • Performance Metrics & Benchmarking Results • Roadmap for implementing in production environment
Specific to Life Science & Healthcare below are some proven use cases for Experimentation:
Patient Dropout and Site Prediction using Adverse Event - Utilizing Adverse Event data along with GenAI to predict patient dropouts and site-related issues in clinical trials, improving trial management and patient retention.
Smart Labelling- GenAI solution that can detect and link Summary of Product Characteristics (SmPC) to the appropriate sections of a corresponding version of a Patient Information Leaflet (PIL) and update the PIL with patient friendly language content that is compliant with regulatory agency standards such as QRD template, Excipient guidelines etc.
Reg Intelligence - Harnessing GenAI to gather, process, and analyze regulatory information, providing insights and intelligence related to regulatory affairs in the pharmaceutical and healthcare sectors.
Audit Automation - AI-driven document auditing system that identifies gaps with enhanced accuracy and quality of output, like summarization compared to the existing system.