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Customizing large language models (LLMs) involves adapting pre-trained models via cloud AI platforms, using tools like agent builders for prompt engineering, RAG data integration, workflows, and guardrails—focusing on configuration over full training.
🔹 Prerequisites
Access to AI platforms with tools.
Expertise: Analysts for basics; developers/data scientists for advanced.
Prepared data for RAG (e.g., vector embeddings).
Governance for privacy, transparency, and costs.
LLM basics: Model selection, prompting, fine-tuning.
Engage partners if needed for skills.
🔹 Steps to Customize
Approach: Modify pre-built agents or build custom; start with pre-built for speed.
Select LLM: Choose from integrated providers (e.g., OpenAI, Google) or via APIs.
Workflows/Tools: Define prompts (e.g., "Summarize text"), add tools for tasks like queries.
RAG Data: Upload documents with usage instructions for context.
Build: Assemble agent; enable feedback for refinement.
Guardrails: Add rules for safety, human review, moderation.
Test/Deploy/Monitor: Simulate, deploy, track performance with analytics.
🔹 Key Features and Examples
Integration: Automates processes like data routing.
Benefits: Scalable adaptation via prompts/RAG; fine-tuning for specialties.
Examples:
Document processing: Extract/analyze with custom RAG rules.
Insights: Natural language queries for summaries/recommendations.
Predictions: LLM simulations with integrated data.
