AI Engineering

Intro to Large Language Models

Understanding LLMs, architectures, capabilities, limitations and real-world applications.

Choosing the Right Model

Model selection criteria, comparisons (Mistral, Llama, GPT, Claude, etc.), cost, performance & trade-offs.

Prompt Engineering

Prompt design techniques, patterns, roles, few-shot, chain-of-thought, system prompts & best practices.

Evaluation

How to evaluate LLM applications, metrics, human evaluation, automated evaluation & benchmarks.

RAG (Retrieval Augmented Generation)

Build reliable LLM apps using your data. Indexing, chunking, retrieval strategies & advanced RAG patterns.

MCP (Model Context Protocol)

Enabling LLMs to interact with tools and external systems securely using MCP.

Vector Databases

Vector embeddings, similarity search, popular vector DBs (Pinecone, Weaviate, Chroma, Qdrant) & use cases.

Deployment

Deploy LLM applications with APIs, Streamlit/Gradio, Docker, Cloud Platforms & serverless options.

Guardrails

Content moderation, safety filters, output control, policy enforcement & responsible AI.

Security

Data privacy, API security, prompt injection, threats & mitigation strategies.