Question 1

When should I fine-tune an LLM vs use RAG vs just prompt engineer?

Accepted Answer

Decision framework: Start with prompt engineering (days, lowest cost). If prompt engineering can't achieve domain accuracy, add RAG (weeks, grounds responses in your data). If RAG can't achieve the right output style or domain knowledge depth, fine-tune (weeks, needs training data). Fine-tuning is best for: consistent output format, domain-specific terminology, and tasks where RAG retrieval quality is the bottleneck. RAG is best for: knowledge-intensive tasks, frequently changing data, and when you need citation tracking.

Question 2

How do you evaluate LLM output quality?

Accepted Answer

Multi-dimensional evaluation: automated metrics (ROUGE, BERTScore for text similarity; custom regex and schema validation for structured output), LLM-as-judge (GPT-4o evaluates Llama output on your criteria), human evaluation (domain experts rate samples weekly), and production monitoring (user thumbs up/down, conversation completion rate, support ticket deflection rate). We build evaluation datasets specific to your domain and track metrics over time — not just at launch.

Question 3

What vector databases do you work with?

Accepted Answer

Pinecone (managed, serverless, best for teams that don't want to manage infrastructure), Weaviate (open-source, hybrid search with built-in vectorization, GraphQL API), Qdrant (open-source, Rust-based, high performance), pgvector (PostgreSQL extension, best when you want vectors in your existing database), and Chroma (lightweight, good for prototyping). Selection depends on: scale (millions vs billions of vectors), latency requirements, existing infrastructure, and team familiarity.

Question 4

How do you handle LLM application security?

Accepted Answer

Security layers: prompt injection detection (classify input as legitimate vs adversarial), output sanitization (remove PII, block harmful content), rate limiting and authentication for API endpoints, sandboxed code execution for AI-generated code, and audit logging for compliance. For enterprise: private model deployment (no data leaves VPC), RBAC for AI features, and data classification before sending to LLMs. We follow the OWASP Top 10 for LLM Applications.

Question 5

Can you build LLM applications that work offline or on-premise?

Accepted Answer

Yes — for air-gapped environments, we deploy open-source LLMs (Llama 3, Mistral, Code Llama) on on-premise GPU infrastructure using vLLM or TGI for serving. Hardware requirements: NVIDIA A100/H100 for large models (70B+), RTX 4090 clusters for smaller models (7B-13B). We also implement hybrid architectures: cloud LLM for non-sensitive tasks, on-premise LLM for sensitive data. RAG works fully offline once the vector database and embedding model are deployed locally.

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