GenAI
Generative AI Methods Supported by TAZI Platform
Example: Traditional AI vs GenAI approach to Customer Complaints Classification
Approach 1: Traditional AI
Approach 2: GenAI
- Model Setup: Choose a Large Language Model (LLM) and set parameters like temperature (to control randomness), context window size (how much text the model considers), and response length. The task is defined in a prompt. Zero-shot learning doesn’t require examples, while few-shot learning uses a few example inputs and outputs to guide the model.
- Classifying New Complaints: To classify a new complaint, combine the prompt (with examples for few-shot learning) and the complaint text as context for the LLM. You can either use a fixed set of examples for all inputs or adaptively select examples based on each new complaint, using methods that identify similarities with previously labeled complaints.
- Adaptive Learning: Zero or few-shot learning models can adapt based on the descriptions and data labeled by domain experts, reflecting their past actions and decisions.
RAG (Retrieval Augmented Generation)
Common RAG Use Cases
A more typical use of RAG is in document analysis for answering questions. For instance, in handling an insurance demand letter, RAG can extract specific information (like provider names or diagnosis codes) and present it in an organized format. It enables users to ask complex questions about documents, as if consulting an expert. Another application could be analyzing educational material or entertainment content to answer related questions.
Application in Customer Complaints:
With TAZI, RAG can be used by leveraging internal process documents instead of traditional training data. Documents are broken down into sections and transformed into numerical vectors using an LLM. When a new record is received, it’s also converted into a vector. The system then finds the most relevant document sections. Another LLM generates a response using the input record (such as complaint in our example), relevant document sections, and guidance on formulating the reply.
Using RAG and AI
Fine-Tuning of the LLMs
How It Works
Applications
Common Uses
GenAI, AI, and Analytics
Combining GenAI with Analytics
AI for Anomaly Detection
Synergy between AI and GenAI
Choosing Between AI and GenAI
Trustworthy AI and GenAI
Human Involvement
Data Privacy
Legal and IT Oversight
Continuous Monitoring
Iterative Improvement
For more details on Trustworthy AI, please see the Presidential Order and EU Trustworthy AI standards.
MLOps and LLMOps
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