Question 1

What's the difference between Generative AI and regular AI development?

Accepted Answer

Regular AI focuses on classification and prediction: is this email spam? What will this customer spend? Generative AI creates new content: text, images, code, music, and video. Generative AI developers work with LLMs (GPT-4o, Claude, Llama), diffusion models (Stable Diffusion, DALL-E), and audio models (Whisper, MusicGen). The engineering challenges differ: prompt engineering, output quality evaluation, cost management, and content safety are unique to GenAI.

Question 2

How do you reduce LLM inference costs for production applications?

Accepted Answer

Multi-layered cost optimization: semantic caching (embed queries, cache similar responses — 40–60% savings), model routing (GPT-3.5 for simple queries, GPT-4o for complex — 30% savings), prompt compression (remove redundancy in context — 20% token reduction), batch API processing (non-real-time tasks at 50% discount), and fine-tuning smaller models for specific tasks (replacing GPT-4o with a fine-tuned Llama 3 for domain-specific work). We typically cut GenAI infrastructure costs by 40–70% from initial implementation.

Question 3

Can you fine-tune LLMs on our proprietary data?

Accepted Answer

Yes — we offer three approaches: RAG (no fine-tuning needed, fastest to implement, works with any LLM), fine-tuning with LoRA/QLoRA (domain-specific adaptation in hours, works with open-source models like Llama 3 and Mistral), and full fine-tuning (for maximum domain performance, requires significant compute). We recommend starting with RAG, then fine-tuning when you need specific output style or domain knowledge that RAG can't provide. All fine-tuning includes evaluation benchmarks to measure improvement.

Question 4

How do you handle AI-generated content quality and accuracy?

Accepted Answer

Quality framework: structured output schemas (Zod/JSON Schema) enforce valid responses, RAG grounds answers in source documents with citations, evaluation pipelines measure accuracy using LLM-as-judge and human review, A/B testing compares prompt versions, and user feedback loops (thumbs up/down) surface quality issues. For critical applications (legal, medical, financial), we implement human-in-the-loop review before content is published or acted upon.

Question 5

What GenAI use cases have you shipped successfully?

Accepted Answer

Shipped GenAI products: AI writing assistant for marketing teams (content generation with brand voice fine-tuning), document intelligence platform (contract analysis, invoice extraction), AI code review tool (PR analysis with security vulnerability detection), customer support automation (RAG-powered Q&A with escalation), AI image generation for e-commerce (product photo enhancement, background generation), and multi-agent research assistant (automated competitive analysis reports). Each production system includes monitoring, guardrails, and cost controls.

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