Foundations: You start by brushing up on Python programming and understanding the ML lifecycle. You design pipelines to process customer data efficiently.
LLMs: You select a pre-trained transformer model and fine-tune it using company-specific data. You evaluate its performance using benchmarks and human feedback.
Prompting & Context: You craft prompts that guide the assistant to respond accurately and manage context for multi-turn conversations.
Systems & Tools: You integrate APIs for deployment and monitor performance using observability tools.
Data & Knowledge: You ensure the assistant has access to updated knowledge bases and uses vector databases for quick retrieval.
Evaluation & Reliability: You test the assistant rigorously, adding guardrails to prevent biased or unsafe responses.
AI Products & UX: You focus on creating a delightful user experience, iterating based on feedback, and ensuring the assistant solves real problems.
Ethics, Safety & Responsibility: You implement privacy safeguards and comply with regulations like GDPR.
Continuous Learning: You stay updated with the latest research and continuously improve the assistant based on user feedback.