AI Automation & Practical Prompt Engineering

• System prompts, few-shot conditioning, Chain-of-Thought (CoT)
• JSON mode & structured outputs validation with Pydantic
• API cost optimization, token management & rate limit handling

• Text embeddings (OpenAI text-embedding-3, Cohere)
• Chunking strategies (sliding window, recursive character)
• Vector DBs (Pinecone, ChromaDB, PGVector) and hybrid search

• LangGraph / CrewAI multi-agent orchestration
• Function calling with external APIs (SERP, Weather, Database)
• Evaluation frameworks (Ragas) and security red-teaming against prompt injections

Q1: What is RAG (Retrieval-Augmented Generation) and why is it preferred over fine-tuning?

RAG dynamically fetches relevant private documents from a vector store at query time and provides them in context. It is cost-effective, prevents hallucinations, and allows instant real-time knowledge updates without re-training.

Q2: How do you prevent prompt injection attacks in production applications?

Use strict system prompt boundary delimiters (XML tags), input sanitization, secondary LLM evaluator guardrails (like Llama Guard), and enforce read-only tool permissions.