Generative AI for a solution architect use cases?
Here are the use cases of generative AI for a solution architect,
•Integrating GenAI models in workflow automation.
•Understanding GenAI life cycle.
•Understanding use cases, LLMops, limitations, ways to improve gen AI tools.
•Using GenAI in day-to-day activities.
•Prepare your own version of GPT model for documentation support
Enterprise LLM LifeCycle:
Enterprise LLM Life Cycle:
1.Ideating and exploring loop
•Understanding business requirements
•Using use case specific foundational models
•Preparing data, prompts and identifying limitations.
2.Building and augmenting loop
•Guiding and enhancing the LLM to meet specific needs.
•Base model trained on enterprise local data, real-time data.
•RAG based SQL and Non-SQL data injection to prompt for better context.
•LLM fine-tuning to adapt with the specific response scenarios.
•Combine Prompt, RAG and Fine-tuning for optimal result.
3. Operation loop
•LLM transition from development to production.
•Deployment, monitoring, incorporating content safety and integrating CICD process.
•Azure AI prompt flow for deployment in AML.
•AML collects production data for monitoring, helps with cost planning and cost understanding.
•Drift detection metrics for quality and safety monitoring.
4. Managing Loop
•Enterprise life cycle for governance, management and security.
•Responsible AI principles.
No the question is, How can you make your LLM Smarter?
There are 3 key ways, they are,
•Prompting
•Fine-tuning
•RAG (Retrieval-Augmented Generation)
| RAG | Fine Tune + RAG |
| Prompt | Fine Tune |
•Access to current info (from RAG + fine-tune)
•Use of specific info (from RAG)
•Reduction of hallucination (more Specific context)
•Content traceability ( known Content source)
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